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• How do I apply Murch's priority order to choosing among AI-generated takes and cut points, and which failures can I accept? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. Generated shots rarely satisfy every continuity criterion; reviewers need a rule for what to give up. Conditions. Multiple candidate takes or cut points for the same beat; labelled inference throughout, built on Murch's list (C3-B597-01). Method [inference]. (1) Emotion: pick the take whose performance or atmosphere is right for the moment, even if later criteria are flawed; reject takes that are clean but emotionally flat. (2) Story: the shot must show the story information (who, what changed); (3) Rhythm: choose the in and out point where the cut feels right, which is a free choice with generated footage because you can trim a clip anywhere inside it. (4) Eye-trace: check that the viewer's eye lands where the next shot expects (face, hand, object) in the first frames after the cut; generated clips often start with the subject not yet in position. (5) Planarity: screen direction and eyeline across the cut (see C3-B571-12 for the 180-degree rule, eyeline match and match-on-...
• What do Nollywood screenwriters say, in first person, about how they get and develop writing work, and is any griot-trained account available? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Nigerian screenwriters' own accounts (2022-2024) describe referral-driven, contract work: writers pitch when producers have no clear brief, writing begins only after schedule, budget and fees are settled (money is the main hold-up), series use writing teams, and episodes are planned as scene-by-scene breakdowns before drafting. These are individual testimonies, not industry data.
• Do the AMIA open-workflows list and OSSArcFlow materials define AV normalisation or conformance checking steps? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  The AMIA open-workflows README is a curated list of links (Archivematica case studies, OSSArcFlow, RAWcooked DPX workflows, MediaConch, NYPL specifications) and states no normalisation targets or conformance rules itself; AV normalisation and conformance parameters have to be taken from tool-level sources such as Archivematica's format policy registry or MediaConch policies (negative result for this list).
• What does the public MPEG V-PCC/V3C reference software (mpeg-pcc-tmc2 release-v25.0) say about its role, build dependencies, how occupancy, geometry and attribute video are encoded, and under what licence? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  MPEG's V-PCC reference software (TMC2, release-v25.0) is an experimental reference, not an efficient implementation: it codes a point cloud as separate occupancy, geometry and attribute 2D video streams using external video codecs selected by codecId (JM, HM, VTM, SHM, or FFmpeg on a branch), with stacked configuration files where later files override earlier ones; its test example expects an external MPEG dataset path. The code is under an ISO/IEC BSD-style licence that expressly grants no patent rights, so patent clearance is a separate question.
• What does Recraft document for SVG output (vector models, Vectorize, SVG background removal), and what is it silent about regarding editability and tracing? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. 'Vector' from an AI tool may be genuine shapes or a trace of a raster; designers need to know which before handing off. Conditions. Recraft API docs, 23 Oct 2026. Method (documented). Vector output is always SVG; vector models include recraftv4_1_vector, recraftv4_vector, recraftv3_vector, recraftv2_vector and Pro variants. Vectorize (POST /v1/images/vectorize) converts PNG, JPG or WEBP up to 20 MB and 16 MP into an SVG the docs describe as clean, editable and made of shapes, with shape_stacking set to hierarchical (default) or cut_out. removeBackground accepts SVG in and returns SVG out. Text can be placed by text_layout on V3 vector models (C3-B593-02). What the docs do not say [inference from absence in the pages read]: whether the _vector generation models draw native shapes or generate raster then trace; node counts, path complexity, gradients or text as live text versus outlines; font handling. Acceptance check [inference]: open the SVG in a vector editor; count paths...
• What exactly does WCAG 2.2 SC 1.4.3 require for text contrast, and how do I apply it as a check to AI-generated posters, title cards and thumbnails? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. Generated backgrounds are busy, and text laid over them often fails contrast. Conditions. WCAG 2.2 Understanding document for SC 1.4.3 (Level AA); it governs web content, so for posters and thumbnails it is a benchmark, not a legal rule. Method (documented). Text and images of text need a contrast ratio of at least 4.5:1, except large-scale text at 3:1; the Understanding text says 18 point text or 14 point bold text is judged to be large; computed values are not rounded (4.499:1 fails 4.5:1); text that is part of a logo or logotype is exempt; the document notes images of text do not scale well because they pixelate. Relative-luminance formula details are in C3-B066-G25-04 and C2-B22-E3-03. Checks on AI graphics [inference]. (1) After setting type (C3-B593-03), sample the background pixels under each glyph box, not the average of the image, and compute the ratio for the lightest and darkest background pixel against the text colour; the worst case must pass. (2) Gradients and...
• What style, preset and seed controls do Ideogram, Recraft and FLUX 3 document for repeatable image sets, and what is missing (e.g. FLUX 3 has no seed)? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. Repeating a look across a series: which knob does each tool give? Conditions. Docs of 23 Oct 2026. Method (documented). Ideogram 3.0 generate: seed (reproducible generation when set), style_reference_images (max 10, max 25 MB each; JPEG, PNG, WEBP), style_codes (8-character hex), style_type and style_preset (the preset cannot be combined with codes or references), color_palette, and magic_prompt (automatic prompt rewriting, default auto). Ideogram 4.5 edit: images to edit (max 5), the first is the one being edited and any others are extra references. Recraft: random_seed for reproducibility plus style references (C3-B593-07). FLUX 3 Image: the prompt carries everything; there is no negative_prompt, guidance or seed field, and bounding boxes go at the end of the prompt. Implications [inference]. (1) Seed reuse gives repeatability only for an unchanged prompt and settings on one model version; it does not carry a look to a different prompt, so for series work use style refere...
• How much do pixels differ across GPUs and browsers for the same WebGL or WebGPU scene, and what tolerance do conformance tests allow? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  A 2012 study rendered one simple antialiased WebGL scene on 270 crowd-sourced machines: the images looked identical but formed 50 distinct pixel-level renders, mostly consistent within a graphics-card model (the text also mentions 51 groups). Current WebGPU conformance tests assume small differences: integer formats compare exactly by default, float and normalised formats can use ULP or fractional thresholds, a canvas colour-space reftest allows up to 2 levels per channel, and WGSL leaves the rounding mode open when quantising. So pixel-exact equality across devices should not be expected; no recent cross-device measurement was found.
• Which palette and exact-colour controls do FLUX, Recraft and Ideogram document, and how should I verify that a generated image kept the brand colours? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. Brand work needs specific colours; prompts alone drift. Conditions. FLUX.2 / FLUX 3 (BFL), Recraft API, Ideogram 3.0 API, docs of 23 Oct 2026. Method (documented). BFL: hex codes name exact colours in FLUX.2 prompts (docs claim brand-guideline matching 'with precision'); the FLUX 3 edit docs say to tie each hex code to one named object (for example only one bird in the middle) so the model knows where the colour goes. Recraft: controls.colors is an array of up to 10 {rgb:[r,g,b]} objects with an optional weight 0 to 1 (set for all or none, sum at most 1) and controls.background_color; the docs call them 'preferable colors'. A palette can also be stored with a style (Create style). Ideogram 3.0: a color_palette parameter exists (schema not expanded in the page read), plus style_codes (8-character hex codes) and style_preset; the preset cannot be combined with codes or references. Verify [inference]. Sample the output in sRGB, cluster to the dominant five to eight colours, an...
• When should I train a style LoRA instead of using reference images, and what do BFL's FLUX.2 klein guide say about dataset size, captions and trigger words? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. A brand look must hold across hundreds of images, offline or at low cost. Conditions. FLUX.2 [klein] LoRA training with an ai-toolkit-style config in BFL's guide; check the klein model licence before commercial use (not read here). Method (documented). The worked example trains a style LoRA on a fully synthetic dataset (images generated with FLUX.1 [dev] and illustrative LoRAs): 27 training images with caption files. Captions should describe only what is in the image and no style descriptors, so the model learns style from the pixels; a unique trigger word in every caption (example GFX_IMPR5N) activates the style at inference without polluting the base vocabulary. Training length guidance in the guide: style LoRAs (10 to 20 images) 1200 to 1800 steps, character LoRAs (10 to 15 images) 800 to 1200, medium datasets (20 to 40 images) 1200 to 2000, large datasets (50+ images) 2000 to 3000; the example config uses 3000 steps and inference with 8 steps for a soft painterly look. ...
• What do practitioner accounts say about Wwise interactive-music transitions, state-driven switching, layering and their failure cases when official Wwise docs are unreachable? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  A 2018 practitioner tutorial on Wwise state-driven music describes transitions that wait for a sync point (such as the next bar to the destination's entry cue) and says a second state change before that point reverts the pending transition if the destination segment has not started; segments added to one state need their own transition rules, so design complexity grows quickly and designs should stay simple; an intensity parameter can drive states through an RTPC curve. A library composer adds that horizontal (re-sequencing) music is tricky because pieces must cut together. These describe practice, not the vendor's specification, for an unstated Wwise version.
• What does the Harlowe 3.3.8 manual say about save slots, loading a save from a different story version, and built-in storylets? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  In Harlowe 3.3.8, (save-game:) returns true or false, so authors must check it; (load-game:) responds to a save that names a passage no longer in the story by offering to delete the save and then continuing without loading. The manual entries read document no save version tag or upgrade hook, so story changes can strand old saves. Harlowe also provides storylet macros for quality-gated content.
• What audio sample rate and channels does Unity Recorder 5.1.7's Movie Recorder write for H.264 MP4 / ProRes, and can I set them? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  In Unity Recorder 5.1.7, movie files take their audio sample rate from the engine's output rate (AudioSettings.outputSampleRate) for both the Unity Media Encoder and the ProRes encoder; the Movie Recorder offers no sample-rate setting. The Media Encoder records Mono or Stereo, ProRes Stereo only. Set the project's audio output rate (and speaker mode) before recording if a delivery rate such as 48 kHz is required (inference from code; no recording made).
• What state does an open-source interactive-video branching component keep, and does it preload or buffer branches? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  H5P Branching Scenario (1.10.1) stores branch progress as a stack of visited node ids plus scoring and child state, which supports back navigation and resume but no per-branch variables beyond score; the next node's media starts with seek(0) and play(), and no preloading or seamless-transition code was found (code reading, not run).
• How do Recraft's style references and style_id work for keeping a set of images on-brand (1-10 references, flexible vs precise match), and what are the rules? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. Producing many images that share one visual identity. Conditions. Recraft API V2/V3/V4/V4.1 (not V4.1 Flash), docs 23 Oct 2026. Method (documented). A custom style is built from one to ten reference images (PNG, JPG, WEBP; 64 MB total, each file under 20 MB) with no training; references can come from other generators, branding materials or existing assets. Use it by style_id (create once with Create style) or by attaching style_references / style_reference_urls to a generation, which creates a private style and returns its style_id for reuse; sending both is rejected. V4 and V4.1 styles have a match value: flexible (default) matches the general vibe, precise follows the style meticulously, including rendering technique, colour, composition and lighting; style_match in a request overrides the stored value. The dedicated V4 Styles models always require a style. Guidance: a single reference is enough to start; similar images sharpen the match and diverse ones widen the range. ...
• What do open AI video tools (Wan2.2 save_video, diffusers export_to_video) actually write to the file: bit depth, encoder, colour tags, resolution? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. A generated clip looks different after export (banding, size change, shifted levels) and nobody knows what the tool did. Conditions. Python tools that write MP4 through imageio/ffmpeg. Method (read from code). Wan2.2 wan/utils/utils.py save_video: tensor clamped to value_range (-1,1), made into a grid per frame, multiplied by 255 and cast with .type(torch.uint8), written with imageio.get_writer(codec='libx264', quality=8); fps default 30 in the signature. diffusers export_utils.export_to_video: float numpy frames become (frame*255).astype(np.uint8); imageio.get_writer with quality default 5 (variable bit rate; 10 highest), optional bitrate, and macro_block_size default 16, where imageio tells ffmpeg to scale the image up to the next size divisible by that number; set None or 1 to disable, with the warning that many players cannot decode odd sizes. Observations: (a) 8-bit output, so banding risk (C3-B586-01/02); (b) float to uint8 is a cast, not documented as rounded or dith...
• What did Smith & Henderson's eye-tracking study find about continuity editing rules (match action, gaze match) and unnoticed cuts, and what does it mean for cutting AI shots? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. Continuity rules are taught as craft; is there evidence that they hide cuts? Conditions. Smith & Henderson (2008): participants watched seven feature-film excerpts (five minutes each, DVD-quality) and pressed a button when they saw an edit; eyes were tracked; edits were hand-coded for adherence to the rules, including Between Scene, Within Scene, Match Action and Gaze Match. Method (reported). Edits constructed according to the Continuity Editing Rules gave greater edit blindness than edits not adhering to them; a quarter of edits joining two viewpoints of the same scene went undetected, a third when the cut coincided with a sudden onset of motion. The Match Action rule is stated in the paper as a smooth cut between two viewpoints of the same action achieved by coinciding the cut with a sudden onset of motion. Only 8.8% of edits coincided with a saccade, and the authors conclude that most unnoticed cuts come from inattentional blindness as viewers follow the narrative. For ...
• What do the VibeVoice authors say about long-form multi-speaker generation: length, speaker count, evaluation, limitations and misuse risks? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem: choose a model for podcast or multi-participant audio. Conditions: dialogue transcripts with speaker labels. Method (VibeVoice technical report): a 7.5 Hz continuous speech tokenizer plus an LLM with next-token diffusion; up to 90 minutes in a 64K context window with a maximum of four speakers. Evaluation (authors'): 8 long transcripts totalling about 1 hour; WER via Whisper-large-v3 and Nemo, speaker similarity via WavLM, and MOS from 24 annotators on realism, richness and preference, each listening to about six hours; comparisons include ElevenLabs v3 alpha and Gemini 2.5 Pro TTS. Stated limits: English and Chinese only; no non-speech audio (background noise, music, sound effects); no overlapping speech; deepfake and disinformation risk; the authors do not recommend commercial or real-world use without further testing and describe the model as research and development only. Failure modes: unexpected audio for other languages; long output drift is not separately measured i...
• How does Sesame's CSM use conversation context for speaker consistency, and what are its documented limits and misuse prohibitions? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem: keep two speakers consistent across turns in generated conversation. Conditions: CSM-1B base model on a CUDA GPU, with Llama-3.2-1B and CSM-1B access. Method (README): CSM generates RVQ audio codes from text and audio inputs using a Llama backbone and a small audio decoder producing Mimi codes. With no prompt it uses a random speaker identity; to keep voices consistent, pass previous turns as Segment objects (text, speaker id, audio) as context, because the README says CSM sounds best when provided with context. The example call sets max_audio_length_ms=10_000, so long outputs have to be built turn by turn. The release is a base model, not fine-tuned to any voice, and it generates audio only (an LLM must write the text). Non-English is weak despite some capacity. Misuse: the README explicitly prohibits impersonation or fraud (mimicking real individuals without explicit consent), misinformation or deception, and illegal or harmful use. Failure modes: voice drift when context...
• How do I place a dry TTS line in a room with an impulse response using FFmpeg's afir filter, and what are its limits? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem: add a measured or generated room to a clean synthetic voice from the command line. Conditions: FFmpeg with the afir filter; a dry mono voice file and an IR file (mono, stereo or multichannel). Method: afir takes the first input as signal and further inputs as FIR coefficients; the documentation's example is ffmpeg -i input.wav -i middle_tunnel_1way_mono.wav -lavfi afir output.wav. Parameters: dry and wet gains, length (portion of IR used), irnorm (norm applied to IR coefficients, default 1, negative values leave the IR untouched), irgain (0 to 1, applied after irnorm), irlink (normalise multichannel IR channels together, default true), maxir (maximum IR duration, default 30 s, range 0.1 to 60 s), minp and maxp (partition sizes, default 8192; lower values reduce latency at CPU cost), nbirs and ir (several IRs switchable at runtime, up to 32), precision auto/float/double. The filter is described as usable for room equalisation, auralization and spatialization. Example (infere...
• Which input modes (first frame, first and last frame, reference images/video/audio, performance video) does each model accept through Runway's API, and which combinations are rejected? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. Continuity workflows (keyframes, references, motion transfer) depend on which inputs a model accepts; mixing modes silently fails or errors. Conditions. Runway Dev API reference fetched 2026-10-23; per-model request bodies are discriminated unions keyed on model, so fields valid for one model are invalid for another. Method (documented fields). Gen-4.5 image_to_video: one first-frame image, ratio list including 1280:720, 720:1280 and 1584:672, duration 2-10 s, outputFormat options (C3-B586-04). Veo 3.1 (via Runway): first frame, or first plus last; last-frame-only is not supported; duration 4, 6 or 8; ratios 1280:720, 720:1280, 1080:1920, 1920:1080; audio boolean default true. Kling 3.0 Pro: first frame, optionally last; when both are given the output aspect ratio follows the first frame; multiPrompt up to 6 segments with total 3-15 s; cfgScale 0-1 default 0.5; audio default true. Seedance 2.5, Wan 3.0 and Hailuo 3: promptImage uses position first/last for keyframe mode or ...
• How should I bring an SDR AI plate (MP4, PNG) into an OCIO/ACES or Resolve colour-managed timeline: as display-referred or scene-linear, and which OCIO roles and rules matter? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. AI plates are rendered pictures with no camera log or scene-referred metadata, yet colour-managed timelines expect an input colour space. Conditions. OCIO v2 configs (docs 'latest' at 2026-10-23, CG config based on ACES 2.1); applies to any OCIO application. Method. OCIO documentation facts: a config has Roles, File & Viewing rules, Displays & Views, Looks, scene-referred Colorspaces, Display Colorspaces and Named Transforms; the CG config says baking in an ACES Output Transform needs a DisplayViewTransform rather than a ColorSpaceTransform, because applying a view transform is a different process from converting between colour space encodings; the data role should use a colour space with data: true so no transforms are applied; the default role is the fallback colour space only when strictparsing is false; ociocheck detects undefined colour spaces and roles; the authoring guide warns that applications vary in how they interpret roles. Workflow [inference, three cases]. (A)...
• What do OpenAI and Google docs say about keeping characters and brand elements consistent across generations, and how many reference images can I give? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. Logos, mascots and products must repeat accurately across images. Conditions. OpenAI GPT Image docs and Gemini (Nano Banana) docs, 23 Oct 2026. Method (documented). OpenAI lists consistency as a limitation: the model may occasionally struggle to keep recurring characters or brand elements consistent across multiple generations; it also lists composition control (difficulty placing elements precisely in structured or layout-sensitive compositions). Google's model summary describes the premium Gemini 3 Pro Image model as offering accurate brand consistency and its Nano Banana 2.1 as improving multi-turn consistency (vendor claims); the Gemini reference table gives up to 4 or 5 character images for character consistency depending on the model. Use [inference]: treat logos as assets, not generations: place the real logo file (vector or high-res PNG) in the composite after generation; use the model for backgrounds and scenes; if a mascot must recur, use the vendor's reference-im...
• What is Murch's Rule of Six as he wrote it (six criteria, the 51/23/10/7/5/4 weights, the sacrifice-from-the-bottom rule), and how seriously did he mean the numbers? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. The Rule of Six is quoted everywhere with varying numbers; an agent needs the original wording. Conditions. The text read is a course page that reproduces pp. 17-20 of Murch's In the Blink of an Eye (1995) and says so itself; it is a copy of copyrighted text, not the publisher's edition, so page numbering and wording should be checked against the book. Method (what the text says). An ideal cut satisfies six criteria at once: (1) it is true to the emotion of the moment, (2) it advances the story, (3) it occurs at a moment that is rhythmically interesting and right, (4) it acknowledges eye-trace, the location and movement of the audience's focus within the frame, (5) it respects planarity, the grammar of three dimensions transposed to two (stage line etc.), (6) it respects three-dimensional continuity of the actual space. Weights: Emotion 51%, Story 23%, Rhythm 10%, Eye-trace 7%, Two-dimensional plane of screen 5%, Three-dimensional space of action 4% (sum 100). Murch says th...
• Which cues tell a listener how far away a voice is (level, direct-to-reverberant ratio, high-frequency loss, vocal effort), and with what numbers? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem: a clean generated voice sounds 'in the listener's ear' regardless of shot scale; placing it at distance needs the right cues together. Conditions: dialogue or narration to sit at a stated distance in a scene. Method (review of the psychoacoustic literature): (1) Level: in an anechoic field level falls about 6 dB per doubling of distance, less in reverberant rooms (about 4 dB per doubling in one auditorium); level is a relative cue, confounded by source level and directivity. (2) Direct-to-reverberant energy ratio (DRR): direct sound falls 6 dB per doubling while reverberant energy changes only about 1 dB per doubling, so DRR falls with distance and listeners hear that as farther; perception is most accurate when level and DRR are both present. (3) Spectrum: air absorption removes high frequencies, and sounds with less high-frequency content relative to low are judged farther. (4) Speech-specific: vocal effort changes the spectrum (shouted speech has more high-frequency ener...
• What does Fréchet Audio Distance measure, how is it computed, and what did its original paper show about correlation with human judgments? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem: judge a set of generated or enhanced audio clips without a clean reference per clip. Conditions: a reference set of good audio and an evaluation set (the sample-size findings below show N matters). Method (Kilgour et al., 2019): extract embeddings with a pretrained audio classifier (VGGish) from both sets, fit a multivariate Gaussian to each, and compute the Fréchet distance between them; lower is better. The paper proposed FAD as a reference-free metric for music enhancement and validated it against artificial distortions; it reports a correlation of 0.52 with human perception ratings versus 0.39 for signal-to-distortion ratio, -0.15 for cosine distance and -0.01 for magnitude L2 distance. The authors note VGGish ignores phase and uses mel-scaled bins, so some distortions could go undetected. Failure modes: it compares distributions, so it cannot judge a single clip; it depends on the embedding and reference set; 0.52 is a moderate correlation. Limitations: designed for en...
• Does KAD fix FAD's Gaussian and sample-size problems, and how large must N be? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem: choose a distribution-distance metric for small evaluation sets. Conditions: audio generation experiments with limited generated samples. Method (Chung et al., 2025): FAD assumes Gaussian embeddings, which often fail (the paper shows non-Gaussian VGGish embeddings), and its finite-sample bias scales as O(1/N), so FAD decreases as N grows and can be manipulated by changing N. Kernel Audio Distance uses Maximum Mean Discrepancy (distribution-free, unbiased) and the kadtk toolkit. Measured by the authors: on Clotho 2 with N from 100 to 3,839 the FAD deviation roughly halves each time sample size doubles, while KAD stays near its asymptotic value; on DCASE 2023 Task 7 Foley systems, KAD reaches a Spearman correlation of up to -0.93 with human ratings against -0.80 for FAD's best case. Failure modes: KAD has larger variance at small N; music-trained embeddings (MERT, CLAP-laion-music) correlated less well than others on Foley. Limitations: authors' own comparison, Foley sound no...
• How can I measure and match colour and brightness between separately generated shots using FFmpeg (signalstats, colorbalance/colorcorrect/colortemperature, normalize, colormap)? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. Generated shots of the 'same' scene differ in exposure, white balance and saturation, and there is no colour chart in frame. Conditions. Two or more SDR clips decoded to the same colour space and range; a reference frame you accept as the look. Method. (1) Measure each clip. signalstats exports per-frame metadata keys such as lavfi.signalstats.YMIN and reports for Y, U, V: min, 10th-percentile (LOW), average, 90th-percentile (HIGH), max; SATMIN..SATMAX; HUEMED and HUEAVG; and YDIF/UDIF/VDIF (difference to the previous frame). Use YLOW/YAVG/YHIGH for black point, mid and white point, and SATAVG and HUEAVG for colour (values are in the file's coded range 0-255 as the doc states). The doc describes the filter as aimed at problems of digitised analog video, so use its numbers as relative comparisons only. (2) Correct. colortemperature (1000 to 40000 K, default 6500, mix, pl preserve lightness); colorcorrect (red/blue shift for shadows rl/bl and highlights rh/bh, saturation -3 t...
• Given the structural rules, how should I cut, extend or loop a generated song to a picture duration without breaking phrase structure? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem. Picture is locked to a duration; the generated song is not. Conditions. Pop/rock cue, structure labelled (manually or by an analyser); the generator offers extend/crop (see C3-B575-06). Method (synthesis, mostly inference). 1) Label sections and grid: sections 8-24 bars, phrases 4 bars, hypermeter 4 (C3-B614-01/05). 2) Choose the edit unit: whole sections, or sr/dc halves, never lone phrases (C3-B614-02). 3) To shorten: drop a repeated cycle (e.g. second verse-prechorus) rather than a mid-section phrase; keep the bridge once before the last chorus (C3-B614-03). 4) To lengthen: repeat the final chorus or insert an instrumental cycle, then take the outro (C3-B614-04). 5) Land key hits by shifting tempo or the section, not by squeezing a phrase (see C3-B007-G1-06). 6) Cross-fade at a grid boundary after a fill, and audition both sides. Failure modes: tempo drift and non-integer bar counts in generated audio; lyric lines cut in half. Limitations. Entirely derived from analytic ...
• If I report FAD or CLAP for generated audio, what must I state and why do I still need a listening test? (inference) active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem: automatic scores look objective but hide settings. Conditions: any report of FAD, KAD or CLAP score. Method (inference from the round's papers): report the embedding and checkpoint, the reference set, N (number of clips and frames), whether bias was corrected, and the prompt set; do not compare FAD across papers with different embeddings or N; use per-song FAD to find outliers rather than as a verdict; use CLAP score only as a text-adherence proxy and name the checkpoint. Add a listening test with hidden reference and anchors (see C3-B570-15 for MUSHRA) because: original FAD correlated only moderately with human ratings (0.52); VGGish correlated poorly with music quality ratings in the Gui study (n=10 raters); and the human-preference study found model-level metrics can misrank individual models. Also keep automatic MOS predictors in context (C3-B570-14). Failure modes: optimising to a metric (Goodhart); reporting a single number. Limitations: no source gives a minimum N or...
• What do ElevenLabs' Text to Dialogue and Studio docs say about request size, determinism, speaker count and paragraph locking for long dialogue? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Problem: assemble long, consistent multi-speaker audio from an API. Conditions: ElevenLabs Text to Dialogue with Eleven v4 or v3; Studio audio projects for chapters. Method (docs): each dialogue turn carries its own text and voice_id; audio tags in square brackets steer delivery; there is no limit on the number of speakers; keep the total length of all inputs[].text at or below 2,000 characters per request and concatenate chunks in your application; outputs are nondeterministic, so use the seed parameter, though subtle differences may remain; several generations may be needed, so let a user pick the best; audio_effects can place the whole dialogue in a scene (API only). Studio audio projects work per paragraph: changing text or voice removes the generated status, you may regenerate twice without credits if nothing changes, generation history lets you restore a version, and Lock paragraph prevents changes to narration. Failure modes: voice or loudness drift across 2,000-character chu...
• Comparison of four period accounts by candidate ID, built from Sargent's pinned chapter and the other accounts' published candidate records (their texts were not re-read; wording is theirs). Fifteen items: agreement in direction on crime as conditional, suggestion over depiction, a... active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Comparison of four period accounts by candidate ID, built from Sargent's pinned chapter and the other accounts' published candidate records (their texts were not re-read; wording is theirs). Fifteen items: agreement in direction on crime as conditional, suggestion over depiction, a young-audience concern, cold blood, avoiding the underworld, and local gates being harsher; differences in the body's name, how its role is described, tone toward it, and the test for acceptable crime; Sargent's unique points are board timing (first print before the cut negative) and the repentant-woman pattern; the 1919 list items (suicide, mischief, lynching, kidnapping) have no counterpart in his chapter. Differences in breadth are differences between authors, not evidence that practice changed.
• How does a film director describe rehearsal, directing actors and collaborator alignment before shooting? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  One director describes his own process: he rehearses (while noting that a celebrated director never did, so there are many ways), holds a three-day 'Look of the Picture Meeting' with all heads of department, and sometimes runs 12-14 takes with a very precise actor to loosen the performance; he also describes a scene he could not get right until late in the sound mix. This is a personal account, not a general rule.
• I have a finished master: what are the handoffs to a cinema/streaming package, to captions and audio description, and to an archive, and which public tool or text checks what at each one? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  Synthesis supported by reviewed steps: the chain from master to archive has distinct handoffs (package to IMF or DCP; adding timed text and audio description; validation; archive packaging), each with its own primary text and open checker, and a validator's pass covers only what it implements (Photon aims to validate IMPs for listed applications; libdcp and ClairMeta check DCP structure; BagIt 'valid' only matches manifest checksums). ClairMeta profiles can downgrade or silence checks, so record the profile with any pass. No single tool spans the chain.
• Overview: when a story written in ink, Yarn Spinner, Twine or articy goes into a game build, what does each step (compile, import, localise, save, record) keep and drop? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  Synthesis supported by reviewed findings: what ships differs from what writers edit. ink ships compiled JSON (comments dropped, tags kept), Yarn Spinner ships line IDs with text in string tables, Twine ships HTML whose specs define no save behaviour, and articy exports JSON with LocaIDs; save formats and compatibility belong to each runtime.
• Overview: when I move a design from a layered source file to a web image or a print PDF, which things travel with the pixels and which stay behind? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  Synthesis supported by reviewed findings: layered files (PSD, ORA, Krita) are working formats whose merged composite is what other tools rely on; each export format carries colour identity differently (PNG chunks with priority, JPEG APP2 ICC segments, AVIF colr box), JPEG has no alpha so transparency must be flattened or carried elsewhere, browsers treat untagged images as sRGB, and print PDF/X needs a declared output condition.
• When I record a playthrough of a story-driven game, which narrative-runtime behaviours follow the capture clock and which do not (typewriter text, randomness, audio)? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  Engines offer fixed-step capture (Unity captureDeltaTime; Godot Movie Maker with --fixed-fps, where visual output stays identical; recorders take engine audio rather than system output), but Unity's capture clock does not affect unscaled time. Yarn Spinner's letter typewriter measures elapsed scaled time (Time.timeAsDouble), so under Unity capture its reveal pace should follow the capture clock (inference, not run). ink seeds its shuffle randomness from the current millisecond when a story state starts and stores the seed in saves, so shuffled text can differ between recording runs unless the seed or state is fixed (inference). No source states a determinism guarantee for narrative engines during capture.
• What empirical evidence exists on Meisner-style partner-focused training, and what does it measure? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  The one empirical study found on Meisner-style training (Sun and Okada, 2023, CC BY) analysed what actors said during exercises: over a course, novice students shifted from describing their partner's behaviour to speculating about the partner's inner state, and professional actors showed more partner connection and balanced communication. Sixteen students joined and twelve completed; the authors flag the small sample, underpowered tests, uncontrolled age and gender, and limited transferability. The study measures communication in training, not on-camera performance, so it does not show that the technique produces better acting.
• What are the edge cases in Beat 2.1.18's renumbering: collisions, the '>' reset marker, omitted scenes and the start value? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  Read statically at commit 3e2b67f8, Beat's scene numbering cannot honour a 'restart numbering here' heading (#n>#): the parser marks resetsSceneNumber only on headings that carry a written number, those headings are routed to the forced-number set, and the reset check runs only for auto-numbered headings, in both the outline and the line-preprocessing paths. Auto numbers avoid forced numbers only by exact string, trying suffixes A to G and keeping the G variant even when it is already taken (a possible duplicate). Omitted scenes get an empty number and do not advance the counter; forced numbers never move it. Beat is GPL-3.0-or-later.
• What rights and attribution metadata does IIIF Presentation carry and how do rights, requiredStatement, provider and metadata differ? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  IIIF Presentation 3.0 splits rights information three ways: 'rights' holds one Creative Commons or RightsStatements.org URI (or a registered extension) and is informative and machine-oriented, possibly shown only as an icon; 'requiredStatement' holds label/value text that must be displayed when the resource is displayed or used; 'provider' gives structured data about who supplied the content. Institution-specific wording therefore goes in requiredStatement or metadata, and a manifest without 'rights' asserts nothing about status. Presentation 2.1 used 'license' (URI) and 'attribution' (text that must be shown) for the same split.
• What do Netflix's published M&E guidelines say about fill, Foley, stems and file naming, and which of those statements are scripted-specific? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  Netflix's general M&E guideline asks for a fully-filled, long-play, discrete M&E whose levels match the original mix (music untouched, effects changed only as needed to fill) and names files by its sound-asset convention (ME for the master; FXStem, MXStem, DIAStem, FFFXStem for stems; GUIDE for the dialogue guide). It never mentions Foley by name. Foley wording appears on the Nonfiction M&E page (a married music stem plus effects stem when there is no Foley budget) and the Near Field 2.0 page (ambience and Foley fill must match the original mix). So fill is judged against the original mix rather than a fixed checklist (producer interpretation, supported).
• In an ISDCF Digital Cinema Naming Convention audio-type field, what do HI, VI (and VIN) mean, and where does the audio language go? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  In the ISDCF Digital Cinema Naming Convention, the Audio Type field marks accessibility audio as HI (Assisted Listening), VI (Audio Description) and SL (Sign Language), e.g. 51-HI-VI. No language is given for those tracks because it is normally the language already stated in the Audio Language field. VIN appears only as the channel label in the CPL MainSoundConfiguration, not as a naming token. ISDCF stresses that DCNC is a convention, not a standard.
• What do Ofcom's Section One rules 1.11 to 1.16 say for children's programmes, and is there a decided case on Rule 1.13 or 1.15? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  Under the Ofcom Broadcasting Code (January 2019 edition), easily imitable harmful dangerous behaviour must not appear in programmes made primarily for children without strong editorial justification (and not pre-watershed without editorial justification), and offensive language must not be used in programmes for younger children except in the most exceptional circumstances (Rules 1.13, 1.15). Ofcom found a 2008 pre-watershed stunt-clip programme in breach of 1.13 for insufficient editorial justification; applying that case to scripted children's drama is an inference. The May 2026 consultation on a Tier 1 Standards Code for large on-demand services keeps existing rules as sufficient for protecting young audiences and adds protections for under-18s who appear in programmes.
• What visual differences and artefacts have practitioners reported with ACES 2.0 compared with ACES 1.x (blue gradients, desaturation, colour break-up), and what do ACES working-group members say about causes and remedies? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  Practitioners on ACEScentral reported in 2025 that ACES 2.0 can over-desaturate and show colour break-ups on some saturated sources (one attributing it to the chroma compression), and a relayed working-group answer says the default trades a pleasing look for every image against reaching display corners and invertibility, with LMTs as the remedy. Statements on the Reference Gamut Compression differ: a working-group member says it remains compatible with no recommendation either way, while another participant recommends keeping it disabled because ACES 2 has its own gamut mapper. The ACES docs claim ACES 2 avoids ACES 1 clipping artefacts even with RGC; the reports bound that claim for particular colours rather than refute it. As of a 17 March 2026 TSC summary, a 'technical' LMT for ACES-1-like contrast was being explored and not published.
• What practical steps and contract wording does Equity (UK) publish for performers asked to be digitally scanned or voice-captured, and what legal framing does it give? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-23
  Equity (UK) advises performers to: ask five specific questions before engagement (face/body scanning, voice capture, synthesised performance, data mining and related uses, each with a description of intended use); limit any capture to one named production; negotiate an AI-training carve-out; refuse in writing at contract stage if unwilling; not sign scanning permissions on set; have scans deleted once the production is complete; and keep up to date and share the advice. Equity says no UK law is written explicitly for AI and points to copyright, performers' rights and data protection. Its vision statement sets eight cloning principles, from consent for past, current and future performances to engagement under collective agreements. These are union advice and proposed clauses; the pages report no outcomes.
• How do optimisation-based drama managers (search, MDP, TTD-MDP) represent plot points, manager actions, player models and the author's evaluation function, and what trade-offs does the survey attribute to each? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-23
  Partially verified: the curator re-read none of the cited sources; S1 rests on the producer's pinned reading. The survey describes search-based drama management (plot points in a precedence DAG; manager actions cause, deny, temp_deny, reenable, hint; player response as normalised plot-point coefficients; a linear evaluation function over story features, optimised with sampling expectimax search), declarative optimisation-based management (the same ingredients solved as an MDP policy offline), and TTD-MDPs (a target distribution over whole trajectories to preserve replayability). It reports the author's evaluation function as the encoding of intent, and notes that the MDP approach appears to narrow the set of stories at the cost of replayability, while TTD-MDPs address replayability but not authoring ease.
• What did the primary drama-management papers actually evaluate, and how can an agent test a branching story or drama manager with what they report? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-23
  Partially verified: the curator re-read none of the cited sources; S1, S3, S2, S7, S4 rest on the producer's pinned reading. The inspected papers evaluate very differently. Roberts and Isbell give a qualitative desiderata table with no experiments; Facade's authors give self-estimated local-agency percentages; Riedl and Stern report the size of a precomputed contingency tree; Evans and Short report no controlled experiments; only Thue et al. ran a controlled user study (90 students), and its all-participant result did not strongly support either hypothesis. Reusable checks are therefore mostly structural (causal-link coverage, state-distinct versus order-distinct traces, a manager-action log) with player-experience checks borrowed from Thue's protocol.
• How did DreamGen use video world models to create synthetic training data for robot policies, and what did it measure? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-23
  Partially verified: the curator re-read none of the cited sources; S10 rests on the producer's pinned reading. DreamGen (NVIDIA et al., arXiv v2, June 2025) fine-tunes an image-to-video model on a target robot, rolls it out from an initial frame plus instruction, recovers pseudo-actions with an inverse-dynamics model or latent actions, and trains a policy on these 'neural trajectories'. Reported results: on a humanoid with GR00T N1, 14 novel behaviours 11.2% -> 43.2% and 13 novel-environment tasks 0.0% -> 28.5% (policy trained only on neural trajectories plus a single-environment pick-and-place baseline); RoboCasa solely-synthetic training reaches 20.6% average over 24 tasks; policy performance rises log-linearly with number of neural trajectories. Costs and limits: 240k RoboCasa samples took 54 hours on 1500 L40 GPUs; initial frames are provided manually; the benchmark's automatic evaluator can hallucinate.
• What do the NVIDIA Cosmos paper and docs actually document for synthetic data generation and agent evaluation, versus only propose? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-23
  Partially verified: the curator re-read none of the cited sources; S5, S7, S8, S9 rest on the producer's pinned reading. The Cosmos paper (v3) lists four uses of a world foundation model (policy evaluation, policy initialization/training, planning, synthetic data) and says it reports no empirical results for them. What it does measure are model-level checks: 3D consistency, physics alignment and post-training results for camera control, robotic manipulation (action-conditioned and instruction-conditioned video prediction) and driving. The docs (observed 2026-10-22) describe workflows with inputs/outputs: Cosmos-Transfer2.5 for two augmentation workflows (Simulations to Photorealism; Scale World State Diversity) with computed control maps; Predict2.5 action-conditioned post-training on Bridge end-effector poses; Reason1 best-of-N rejection sampling with a score = fraction of artifact-free evaluations. The docs show example videos and procedures but no downstream policy or accuracy re...
• Before using a generated world (Marble splat or mesh, HunyuanWorld layers, WonderWorld splat) for previs or as an LED-volume background, what should an agent check, what does the one documented LED-volume use report, and what is unknown? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-23
  Partially verified: the curator re-read none of the cited sources; S25, S20, S9, S15, S16, S21, S5 rest on the producer's pinned reading. The only documented LED-volume use found is a vendor case study (Volinga, Feb 2026): a music video used Marble worlds shown as Gaussian splats in Unreal via the Volinga plugin, with stYpe camera tracking and set extension; the vendor and its customer report time saved and on-set switching, with no data on colour, resolution, latency or artifacts. World Labs' docs add plugin notes for nDisplay and depth of field. WonderWorld's authors say its own results have low density and floaters and suggest it as a coarse prototype to refine. The checks below are proposals synthesised from these statements, not tested procedures.

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