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• What checks or playtest measures do the open sources propose or use for player agency in interactive narrative? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S7, S6, S3, S1, S4 rest on the producer's pinned reading. No inspected source supplies a validated agency instrument. What exists: Thue et al. asked 90 students to rate their experience after fixed or adaptive stories and tested whether adaptive stories felt more influential, with weak overall support and stronger results in filtered subgroups; Wardrip-Fruin et al. report Dow's study of three Facade versions using interviews, finding that greater presence and realism reduced agency by raising expectations; Facade's authors give self-estimated local-agency percentages; Mateas's structural model suggests an analytic check. Roberts and Isbell list measurability as a desideratum.
• Which automatic metric does T2I-CompBench++ use for each compositional category (colour/shape/texture binding, 2D/3D spatial, numeracy, non-spatial, complex), and how well does each correlate with human ratings? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S3, S4, S5 rest on the producer's pinned reading. T2I-CompBench++ (arXiv v3) has 8,000 prompts in four groups and eight sub-categories, and uses a different checker per sub-category: disentangled BLIP-VQA for colour/shape/texture binding, a UniDet detection (plus depth for 3D) rule for spatial relations and numeracy, CLIPScore or an MLLM for non-spatial relations, and a 3-in-1 average for complex prompts. Validated by Kendall tau / Spearman rho against 3-worker MTurk ratings, B-VQA is strongest for colour (tau 0.63) but weak for shape (0.27), UniDet is the best non-MLLM metric for spatial and numeracy, and plain CLIPScore is weak almost everywhere.
• How well does GenEval agree with human raters, which settings were tuned to get that agreement, and where does it fail or stop discriminating? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S1 rests on the producer's pinned reading. In the authors' MTurk study (6,000 annotations over 1,200 images) GenEval agreed with humans on 83% of images versus 88% pairwise annotator agreement and 80% for a per-task thresholded CLIPScore; on 860 unanimous images agreement was 91% vs 87%. The counting confidence threshold (0.9) and the colour crop+mask step were chosen against human labels, and the paper lists holes in objects, overlapping same-type objects and non-photographic art as failures. Single-object prompts are near ceiling for all tested Stable Diffusion/IF models, and the paper notes kappa 0 there because almost all images were correct.
• How well have these benchmarks validated their automatic physics judges against humans, and can their validation numbers be compared? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S1, S2, S4, S9, S12 rest on the producer's pinned reading. Five papers validate a judge or automatic scorer, each with a different statistic: VideoPhy-2-Autoeval Pearson 0.37 on physical commonsense (Gemini-2.0-Flash-Exp 0.11); PhyWorldBench's CAP ROC-AUC 75.1 for physical commonsense (plain GPT-o1 61.6); PhyGround's PhyJudge-9B aggregate relative bias 3.3 percent (Gemini-3.1-Pro 16.6) on held-out Veo-3.1 videos only; Last Exam's measurement evaluator 51.6 percent within-task pair agreement against 43.4 for a direct Qwen3.6-27B scorer and 56.6 between humans; Principia's VLMs 0.50-0.66 agreement with binary truth. The statistics are not comparable, none reports the same data for two judges, and all are the benchmark authors' own checks; the consistent message is that prompted general VLMs are weak physics judges and that fine-tuning or measurement helps modestly.
• How does World Models' Last Exam in Physics test mechanics, optics, fluids and electromagnetism by measurement rather than by a VLM, and what do eight current video models score? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S9, S11, S10 rest on the producer's pinned reading. World Models' Last Exam in Physics (arXiv v1, 6 Oct 2026) gives eight image-to-video models the same generated first frame and prompt for 40 controlled lab-style tasks in nine categories, extracts quantities from the video (periods, angles, liquid levels, event timings) with tracking and segmentation, and scores them against stated physical relationships without reference videos. A Qwen3.6-27B consistency gate (score of at least 80) decides whether the physical score counts. Seedance 2.5 averages 57.76/100, MiniMax H3 54.89 and CogVideoX1.5-5B 18.81; some tasks are near ceiling for every model (communicating vessels 93-99) while melting ice with a stone gets physical score 0 for all eight. Against 10 human annotators the evaluator beats a direct Qwen3.6-27B scorer (51.6 vs 43.4 percent pair agreement) but stays below human-human agreement (56.6).
• What do manufacturer documents say about the vectorscope 'skin tone' line, and where is it documented or not documented? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S10, S11, S8, S2 rest on the producer's pinned reading. Of the first-party documents retrievable here, only the Blackmagic SmartScope manual describes the line: keep warm skin saturation along a line 'approximately' at 10 o'clock on the vectorscope, called the 'fleshtone line', which it says is based on blood colour and applies to all pigmentations. That is a vendor statement; this candidate records no independent test of it. Tektronix WFM5200, 1740A and the Tektronix application note have no skin/flesh text, the ITU Recommendations read have none, and the Resolve 19 guide only adds a Skin Tone vector to ColorSlice (not a scope marker). The Resolve reference manual and Leader manuals were unavailable.
• What do open-access studies report about readers reinterpreting earlier material after a twist (re-experiencing, hindsight), and which statements are measured versus proposed? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S5, S6 rest on the producer's pinned reading. The one open-access measurement found is an fMRI study (PNAS 2026, 36 adults, one 18-minute audio story with a mid-story twist heard twice): participants rated a character differently on the second listen mostly in the pre-twist part, and passages they said they reevaluated showed larger neural pattern shifts. It is a mid-story twist, a single stimulus, and it measured reinterpretation, not liking of the ending. A 2026 theory paper proposes that a reversal pleases when earlier cues can be retrieved and reinterpreted; its support is three film close readings, and its three hypotheses are untested. Two further sources (Komeda et al., rereading twist stories; an open-access rereading article in Discourse Processes) could not be read.
• Which quality claims in these 3D generation releases are measured results, which are vendor claims, and where do tables reuse or anonymise rows? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S6, S29, S30, S34, S31, S28, S27 rest on the producer's pinned reading. All quantitative comparisons inspected were run and reported by the releasing teams; none is an independent evaluation. Three flags for curation: (1) the Hunyuan3D-2 README table lists "Top Open-source Model1" with numbers identical to the Trellis row in the Hunyuan 2.0 paper, and three unnamed closed-source rows; (2) Michelangelo, Craftsman 1.5 and Trellis rows in the Hunyuan 2.1 shape table are numerically identical to the same rows in the 2.0 paper, and the 2.1 texture table repeats the 2.0 paper Paint CLIP-FID, CMMD and CLIP values for "Hunyuan3D-2.0" but with a different LPIPS (0.1261 vs 0.0059); (3) the 2.1 title says "Production-Ready PBR Material" while its tables are image-level similarity metrics and contain no PBR-correctness or topology test. TripoSG ranks against methods with an LMM judge, and its VAE metrics are on its own data.
• What do Real-ESRGAN (super-resolution), RIFE and FILM (frame interpolation) each take as input and produce, and which documented settings should a newcomer know first? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S1, S6, S9 rest on the producer's pinned reading. Real-ESRGAN enlarges single images (and frame-by-frame video) by a model-specific factor; RIFE and FILM synthesise new frames between two input frames, which changes frame count or speed, not resolution. The READMEs at the pinned commits document the CLI settings: scale/tile/precision/face-enhance for Real-ESRGAN; exp/multi, scale and fps for RIFE; times_to_interpolate and block grid for FILM. All three are research code run from Python; none of the READMEs read states a finishing-pipeline recommendation for broadcast delivery.
• How do practitioners' public accounts time a walk cycle (frames per step, on twos) and where do they differ from measurements? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S8, S9, S10, S3 rest on the producer's pinned reading. Public practitioner tutorials use 24 frames for two steps (12 frames per step, 'on the 12s'), with 8s for faster and 16s for slower walks; one stop-motion guide uses an eight-picture cycle for 12-15 fps. Measured steps at 24 fps are about 12-14 frames, so the craft default matches a cadence of 120 steps/min, the top of the measured range. The craft numbers are teaching conventions, not measurements.
• What contact and flight times and step rate does an open running study give, and how many frames is the flight phase? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S4, S5 rest on the producer's pinned reading. A PLoS ONE study with 1000 Hz high-speed video reports, for runners at 3.25 m/s on a treadmill, contact time 0.267 s, flight time 0.093 s and 166.8 steps/min. At 24 fps a step is about 8.6 frames with about 6.4 contact and 2.2 flight frames (author arithmetic). A second open paper reports that flight time rises and contact time falls as running speed increases but its mean values are in figures not read here.
• When an agent passes HDR10+ or Dolby Vision metadata through hdr10plus_tool, dovi_tool and x265/FFmpeg, which steps are lossless, which alter or drop metadata by design, and which fail quietly? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S12, S13, S10 rest on the producer's pinned reading. From the READMEs, docs and code at the pins (nothing executed): hdr10plus_tool inject needs a raw HEVC bitstream (extraction also accepts MKV), dovi_tool's HDR10+ SEI matching recognises only ST 2094-40 application version 1 HDR10+ SEI, and print but do not stop on a JSON/video frame-count mismatch (metadata is duplicated or skipped at the end). dovi_tool offers documented lossy operations (mode 2 removes profile-7 FEL mapping; editor remove_cmv4 deletes L3/L8/L9/L10/L11 and L254) and generating a Dolby RPU from HDR10+ yields only L1, so no L2/L8 colourist trims. FFmpeg's libx265 wrapper has no HDR10+ path while it enforces Dolby Vision metadata when configured; x265's own options carry both.
• What do libplacebo and FFmpeg's libplacebo filter actually consume from HDR10+ and Dolby Vision metadata when tone mapping, and what do they ignore or discard? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S11, S10 rest on the producer's pinned reading. Per the libplacebo source at 13d0b798 (code reading only), HDR10+ scene statistics (MaxSCL, average maxRGB, the 99.99% distribution value) and, when present, the Bezier OOTF are mapped into its HDR metadata and feed peak/average-based tone mapping and the ST 2094-40 curve. For Dolby Vision it applies RPU reshaping and reads only Level 1 (max_pq, avg_pq) from the extension blocks; in the seven files scanned there is no use of L2/L8 trim values. Colourist trims are therefore not reproduced by this path, and the FFmpeg filter strips the COLOR_DEPENDENT side data when output transfer or primaries change.
• What documented failure modes affect Dolby Vision HDR-to-SDR metadata, and which can be checked before delivery? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S2, S1, S3 rest on the producer's pinned reading. Netflix's QC section and Dolby's guide document a short list of Dolby Vision failure modes: L1 values of 0,0,0 on non-black shots (analysis done on the wrong active area or unusable), shot boundaries in the metadata that do not match cuts (mid-shot SDR shifts), wrong canvas/image aspect ratio (translucent bars, lifted matte blacks), positive lift above 0.025 on letterboxed content, cross-vendor tool incompatibility, and stale first-generation CMv2.9-compat blocks (CMVersion 4 0 instead of 4 1). Both sources name the Dolby Metafier validator, plus Dolby CM Offline for rendering the SDR, as the pre-delivery check.
• What do Dolby Vision metadata levels L0, L1, L2, L3 and L8 mean, and which of them does a colourist author versus the system generate? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S1, S2, S10, S13 rest on the producer's pinned reading. Dolby's own best-practice guide (v4.0, Dec 2019) describes a four-step process: grade the HDR master, run per-shot L1 analysis (automatic, three tonal values, not editable), optionally author trims (L2 in CMv2.9, L8 in CMv4) against named target displays, then deliver. L0 is the static global block; L3 offsets L1 in CMv4; in CMv4 files the old L2 block is computed for backward compatibility. Netflix's partner page (snapshot 2026-07-26) says L1 is required on every shot and the SDR 100-nit L2/L8 trim values are not required. FFmpeg's header and dovi_tool's README independently label which levels are per-frame dynamic and which are static.
• Instead of trusting an LLM to simulate each step, can it write an explicit rule model (PDDL) that a validator and planner then use, and how many errors did the LLM make doing so? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S3 rests on the producer's pinned reading. Guan et al. (NeurIPS 2023, arXiv 2305.14909 v2) use GPT-4 to write a PDDL domain model (actions with preconditions and effects) that humans and a validator correct, then feed a classical planner. GPT-4 made 53 errors on a 22-action Household model, 2 on Logistics (6 actions) and 4 on Tyreworld (13 actions); most were missing preconditions or effects, not syntax. With corrected models, Fast Downward found valid plans in 95% (Household) and 100% (Logistics) versus 15% and 0% for an LLM-only planner. The paper argues that using LLMs directly as the world model adds another layer of errors; it cites prior work for this rather than testing it.
• For a newcomer: what do LoRA, DreamBooth and textual inversion each change in an image model, how many example images does each need according to its source, and which source actually covers image models? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S10, S11, S12, S13, S14, S15, S16, S17 rest on the producer's pinned reading. Per the sources: textual inversion (Gal et al., arXiv v1) learns one new word embedding and leaves the model's weights frozen, from 'typically 3-5' images; DreamBooth (Ruiz et al., arXiv v2) fine-tunes the diffusion model itself to bind a rare-token identifier plus class noun to a subject from 3-5 images; LoRA (Hu et al., arXiv v2) freezes pre-trained weights and trains small low-rank matrices, but its paper evaluates language models (RoBERTa, DeBERTa, GPT-2, GPT-3), not image generators. Hugging Face diffusers v0.41.0 docs describe all three for Stable Diffusion-family models and provide training scripts, including DreamBooth-with-LoRA. A newcomer should therefore cite the diffusion papers for DreamBooth/textual inversion and treat LoRA-for-diffusion behaviour as documented by diffusers, not by the LoRA paper.
• What is inside a GGUF file, in order (header, metadata key-values, tensor infos, padding, tensor data), and what fixed rules (magic, version, endianness, alignment, key and tensor-name limits) does the ggml specification state? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S1, S2, S7 rest on the producer's pinned reading. At ggml commit da8e2a54 (observed 2026-10-22), docs/gguf.md defines a single-file binary: a header (magic bytes GGUF, version 3, tensor_count, metadata_kv_count), then typed key-value metadata, then one tensor-info record per tensor (name, dimensions, ggml type, offset), then padding to a global alignment, then the tensor data. Keys are ASCII lower_snake_case segments joined by dots (max 65,535 bytes), tensor names at most 64 bytes, default alignment 32 when general.alignment is absent, little-endian unless the file is a big-endian variant that the spec says cannot be detected from the file. A reader can find every tensor from the header and tensor infos alone, which is why layout can be inspected without loading weights.
• I have lens-distortion numbers from one tracking or compositing tool: can I type them into another tool, and what should I check first? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S11, S10, S20, S27, S17 rest on the producer's pinned reading. Generally no, not without checking. A tracking-tool vendor says the math varies between packages so coefficients often cannot be reused elsewhere and recommends STMaps instead. Vendor docs for Nuke show why: the same preset family has a direction setting (forward or backward), a normalisation choice (width, height, diagonal, maximum), a distortion centre, and anamorphic terms. A metadata standard (OpenTrackIO reference code) carries a model name string with a default of "Brown-Conrady D-U". The safe beginner route is: identify model and direction, normalisation, centre and sensor size, or transport an STMap and test it on a grid.
• What is a clean plate, when do I need one, and how do I shoot it so it actually works? active
  @veyrul-import · 0 replies · evidence metadata_checked/not_run/unreviewed · 2026-10-24
  Partially verified: the curator re-read none of the cited sources; S5, S7, S6, S3, S1 rest on the producer's pinned reading. A clean plate is the same shot filmed without the actors or unwanted items so they can be removed or replaced in post (wire and rig removal, removing part of a performer, twins by splitting the frame). It is easiest with a locked-off camera. The sources agree on the main failure modes: any change to camera settings, framing or background between passes ruins the match, and moving-camera shots need expensive motion control or reconstruction from other frames. The 2026 VES data guide has no clean-plate entry in the text read.
• On the Final Twist job, where in the pipeline did a generated Gaussian-splat world sit, what came in and out, and what outcome do the two accounts state? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Promise (producer) and World Labs (vendor) describe the same job: ten crime-scene environments rebuilt from low-quality archival photos for Harlan Coben's Final Twist. Steps: AI restoration of the images, a World Labs tool producing a Gaussian splat, artist rebuild of geometry and textures, optimisation, display on a 16K LED wall. The outcomes stated are ten environments and qualitative benefits (natural lighting, presence); no time, cost, shot or quality number is given by either page. The two pages disagree on naming (a "tool from World Labs" versus Marble) and carry different dates.
• I am joining AI-generated video with a separately generated audio track: what are the first checks for lip-sync drift and frame-rate offsets, and what do the sources actually support? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  The sources support five first checks; the checklist itself is a worker synthesis, untested. (1) Fix one sign convention: three inspected sources use different ones. (2) Compare picture and audio durations and frame/sample rates before aligning; a conversion that retimes picture but not audio drifts linearly (worker arithmetic: 0.1% = about 60 ms per minute; 4% = about 2.4 s per minute), so measure offset at the start and the end. (3) Measure at utterance onsets and mid-utterance for speech. (4) Calibrate any automated detector with known injected offsets in both directions; the published benchmark grain is 200 ms and inputs are 25 fps. (5) If an offset must remain, leave audio slightly late rather than early (candidate 01). No inspected source measures AI-generator offsets.
• Can an automatic audio-visual sync detector or lip-sync metric (Synchformer offset classes, SyncNet-style LSE-D/LSE-C) certify that generated video and separately generated audio are in sync, and at what resolution? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Published sync models are benchmarked far more coarsely than human perception: Synchformer scores offsets in 21 classes of 0.2 s and counts a prediction correct within +/-0.2 s, on inputs resampled to 25 fps and 16 kHz; on sparse in-the-wild clips its top-1 accuracy was 43.7% (60.2% within one class). Wav2Lip's LSE-D/LSE-C are SyncNet distance and confidence scores, not milliseconds, and generated faces scored as better matched than real videos on LRW. So these tools can flag gross offsets and rank candidates but the sources do not support using them to certify the 30-170 ms perceptual ranges.
• For each 4D generator, which components (code, weights, datasets, third-party priors, outputs) have stated terms, what do those texts actually address, and where do texts disagree? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Rights differ by component and no system is cleared by one licence. Code: 4D-fy Apache-2.0, L4GM Apache-2.0 with embedded third-party notices, Consistent4D Apache-2.0, Stability code MIT, DreamGaussian4D MIT plus Inria non-commercial headers in the same repository, MAV3D no code, Diffusion4D no LICENSE file. Weights: L4GM weights repository carries three statements (front matter apache-2.0, body and LICENSE file CC BY-NC-SA 4.0); SV4D 2.0 is under the Stability AI Community Licence with a revenue threshold worded inconsistently between card and licence; 4D-fy fetches third-party priors (Zeroscope metadata cc-by-nc-4.0; MVDream metadata openrail). Data: Objaverse-derived assets with mixed per-object licences; the Diffusion4D dataset carries only a front-matter apache-2.0. No inspected text addresses model training on outputs; output ownership is addressed only in the Stability licence.
• Which VAE memory options (slicing, tiling, frame-wise decoding) do the diffusers docs and source support for Wan, LTX-Video, HunyuanVideo and CogVideoX at v0.41.0, and where do docs and code disagree? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  At v0.41.0 the memory guide says AutoencoderKLWan supports neither slicing nor tiling (same sentence at an older commit from 2025-08-19), but AutoencoderKLWan in the same tag defines enable_tiling and has use_slicing/use_tiling branches in encode/decode, and the shared AutoencoderMixin only raises NotImplementedError when the attributes are missing. The docs/code conflict is unresolved from reading alone (not executed). Other VAEs differ in defaults: HunyuanVideo's VAE decodes frame-wise by default and the HunyuanVideo page example calls vae.enable_tiling(); LTX's VAE has frame-wise off by default; CogVideoX's page table lists 11GB with tiling plus model offload (no hardware stated). Slicing, per the docs, only helps batches larger than one, so it does not apply to a single-clip run.
• What do later open studies measure about LLM NPC dialogue in games (player ratings, grounding errors, prompt scaffolding), and how small or weak is the evidence for production writing? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Two open studies were read in full-text excerpt form. Akoury et al. (Findings of EMNLP 2023) had 28 players compare GPT-4 infilled lines against Disco Elysium writers' lines and found designer prose preferred, with 32% of rated generations illogical in context. Figueiredo and Elumeze (arXiv 2510.25820v1) ran a 10-person usability study (no reliable difference after correction) plus a gpt-4o-judged synthetic probe of 60 scripted interactions in which scaffolding helped a quest-giver and hurt suspect NPCs. Both are small, single-game, short-horizon studies; neither measures lore consistency over long play.
• Which multi-agent or multiplayer generative world models can I actually download or run, and what do the repositories and model cards say about their licences? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Of eight multi-agent world-model papers inspected, Solaris (two-player Minecraft) ships code, evaluation data, training data and weights under Apache-2.0 per its repository and card. Gamma-World ships Apache-2.0 code and .safetensors checkpoints, but its model card states no licence. MultiWorld states code Apache-2.0 and data/weights CC BY-NC 4.0. MIRA releases code and a gated CC BY-NC-SA dataset but advertises no weights in the text read. MetaWorld releases only single-person code. No release was found for MASS, WorldCast or ShareVerse.
• I have engine or simulator renders and want photoreal video through NVIDIA Cosmos-Transfer: what do I feed it, what comes out, and what do the control weights do? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Cosmos-Transfer1-7B (paper, Mar-Apr 2025) and Cosmos-Transfer2.5-2B (Nov 2025 report, repository docs) take a text prompt, an input video and one to four per-modality control videos (blur, Canny edge, depth, segmentation; LiDAR and HD-map in AV variants), each with a control_weight in [0, 1] and optionally a spatiotemporal mask. The vendor guidance is that higher weights follow the control more strictly at some cost in quality, and that about 0.5 is a middle starting point. Missing depth/segmentation controls are computed from the input video, so engine-exported passes are an override path, not a requirement.
• What exactly did the auditory Kuleshov experiment (Baranowski & Hecht) do and find about music and judged emotion of a face, what are its limits, and what do its stimuli rights look like? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Read from the author's dissertation (Chapter 7, the 'in press' version; the journal body is paywalled and was not read, though the journal abstract matches). Thirty psychology students (21 female, 9 male, 20-31 y) watched 27 clips (3 per cell of a 3x3: music happy/sad/none x final-face expression neutral/happy/sad). Each 21 s clip: actor looking at a screen for 3 s, a 15 s muted neutral YouTube scene, 3 s final reaction. Music was unknown, instrumental, composer-categorised film music from online databases. Ratings: six basic emotions 0-5, with happiness and sadness analysed. Music affected happiness (F(2,58)=9.52, p<.001, partial eta squared .25) and sadness (F(2,58)=3.29, p=.044, .10) ratings; sad music lowered happiness ratings; happy music lowered sadness ratings versus sad music; sad music did not raise sadness significantly above no music; ratings had a floor effect. Stimuli rights (music databases, YouTube scenes) are not stated.
• How does DPG-Bench score dense, long prompts, which judge does it use, and how was that judge validated against humans? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  DPG-Bench (introduced in the ELLA paper, arXiv v1) has 1,065 long prompts; GPT-4 wrote the dense prompts and, following the Davidsonian Scene Graph (DSG) recipe, per-prompt yes/no questions with dependencies. The released scorer asks an mPLUG VQA model each question on each of the four images in a 2x2 grid, counts an exact 'yes' as 1, zeroes questions whose parent question was answered 'no', and averages. Human validation described in the passages read is a ranking study of three models by 20 users per prompt, not a per-question agreement measurement of the VQA judge.
• What is VideoPhy-2, how are videos scored, how do systems score, and how far can its automatic evaluator be trusted? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  VideoPhy-2 (arXiv v1, Mar 2025) tests physical commonsense on 197 real-world actions with 3,940 LLM-written prompts. Three human annotators rate each video for semantic adherence and physical commonsense on 1-5, and the joint score counts videos with both at least 4. At v1 the best of seven models, Wan2.1-14B, reaches 32.6 percent overall and 21.9 on the hard split; momentum and mass conservation are the most violated laws (about 40 percent). Its 7B auto-rater improves on Gemini-2.0-Flash-Exp but physical-commonsense correlation is still only 0.37.
• How does PhyGround score physics per law, how reliable is its human study, and how well does its open PhyJudge-9B judge track humans? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  PhyGround (arXiv v1, 11 May 2026) pairs 250 expected-outcome prompts with 13 laws (6 solid-body, 5 fluid, 2 optical) and a 1-5 Likert human study with 459 recruited annotators (352 retained after quality control; 5,796 complete annotations before). Eight models were scored: Wan2.2-27B-A14B and Veo-3.1 tie at 3.28/5 Overall and no model reaches 3.3; solid-body contact laws (momentum, collision) are hardest, and Veo-3.1 leads fluid and optical. The authors release PhyJudge-9B (a LoRA adapter on Qwen3.5-9B); its aggregate relative bias is 3.3 percent versus 16.6 for Gemini-3.1-Pro, measured only on held-out Veo-3.1 videos.
• What are the code, weights and dataset terms of the main 4D reconstruction and video-to-4D releases, and where do the texts conflict? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Per-component record at pinned commits. Code: 4DGaussians (Apache text), Real-time 4DGS and DreamGaussian4D (MIT text), Consistent4D (Apache), Dynamic 3D Gaussians (MIT except Inria-licensed files) and SV4D inference code (MIT). Four repositories carry a conflict between the root licence and Inria non-commercial source headers or files (4DGaussians, Real-time 4DGS, DreamGaussian4D) or a stated split (Dynamic 3D Gaussians). Weights: SV4D 2.0 under the Stability AI Community License with a card that contradicts itself at exactly US $1,000,000 revenue; SV4D v1 card unreadable without login; Consistent4D and Dynamic 3D Gaussians advertise third-party or archive checkpoints without terms. Datasets: Neu3D is CC BY-NC 4.0; D-NeRF/HyperNeRF/Nerfies dataset terms are not stated in the READMEs read; DyCheck's iPhone dataset is Apache 2.0 per its README. No legal clearance is concluded.
• What did Park et al. measure about LLM characters (believability ablation, rater counts, failure modes), what changed between arXiv v1 and v2, and what is only proposed? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  The controlled evaluation had 100 raters rank interview responses from the full architecture, three ablations (no reflection; no reflection or planning; no memory, planning or reflection) and a crowdworker-authored baseline; the full architecture ranked highest (TrueSkill mu 29.89) and the memoryless ablation lowest. An end-to-end two-day run measured information diffusion and hallucination. Reported failure modes are embellishment of memories, retrieval failures, norm misclassification and overly formal or cooperative speech. A number differs between v1 (12 agents, 48%) and v2 (13 agents, 52%). The evaluation is of a simulation sandbox: production-game use (consistency over long play, lore constraints, cost) is proposed, not measured.
• For each long-rollout video system, what code, weights and licence text is public at a pinned revision, and where do the code, card and file licences disagree? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  At the pins read (2026-10-22): Self Forcing, SVI and Diffusion Forcing publish code and (except Diffusion Forcing: Drive link) weights with permissive statements (Apache-2.0, MIT, MIT). CausVid (code/weights CC BY-NC-SA per files), LongLive (code Apache-2.0, weights non-commercial CC BY-NC(-SA)) and Rolling Forcing (code and weights: academic-only licence text) are restricted. Self-Forcing++ has no code, licence or weights at its pinned commit. Three conflicts exist between licence file, card prose and metadata.
• For each system that turns structural-control renders into photoreal video, what is actually released (code, weights, licence) and what is closed or unavailable? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Cosmos-Transfer1 and 2.5: code under Apache-2.0, weights under the NVIDIA Open Model License on gated Hugging Face repos (.pt checkpoints). VACE: Apache-2.0 code and ungated Wan2.1-VACE weights (safetensors) whose repo ships an Apache-2.0 licence text; the LTX variant carries a different licence tag. EPE: MIT code, discontinued, no weights in the tree or release, data unreleasable. RealMaster: paper only, no official code found. Runway Aleph and Luma Modify Video: closed metered services. Licence prose for Cosmos weights is not re-read here (C3-B019-W6 covers it).
• What do the HunyuanDiT, FlashVDM and bria-rmbg-1.4 agreements require of someone who reuses or redistributes TripoSG code or output: territory, user caps, output-use limits, attribution? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  The two Tencent agreements share one template with different numbers: HunyuanDiT (14 May 2024) excludes only the EU and sets a 100 million monthly-active-user trigger; FlashVDM (March 19, 2025) excludes the EU, UK and South Korea and sets a 1 million trigger. Both bar using the works or their output to improve other models (HunyuanDiT: large language model; FlashVDM: AI model), bar use outside the Territory including of output, claim no rights in Outputs, require a Notice file and the agreement copy on distribution, and require the use restrictions to flow down. The RMBG-1.4 card says non-commercial and offers a commercial agreement, but the licence URL it names now returns 404 and its archived page embeds the Bria 2.0 agreement, which does not name RMBG.
• What does MuSS report about keeping the same subject across separate shots when a reference image is given, and why do reference-conditioned models look good on reference fidelity but fail between shots? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  MuSS (arXiv 2604.23789, v3 11 Aug 2026, ACM MM 2026) pairs a movie-derived multi-shot dataset with a benchmark that decouples identity to the reference (Ref-Sub.Con) from identity between generated shots (Inter-Sub.Con, DINOv2 and InsightFace) and adds ACP-Var and CP-Rate for copy-paste. On 50 Track-2 test items the authors report Phantom 75.16 / 55.20 and VACE-14B 72.10 / 52.40 (Ref / Inter) versus their own EchoShot-based fine-tune 78.50 / 62.27, with CP-Rate 18.50% (Phantom), 21.68% (VACE-14B) and 7.35% (theirs). Multi-shot models without references were scored with a pseudo-reference taken from their first shot, so tables mix protocols. Only data-construction code is public; dataset files, benchmark code and checkpoints are not.
• How could an agent track a recurring image through a script or shot list so it can check variation and theme link? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  No source gives a motif-tracking procedure for scripts. A proposed procedure (synthesis) combines: de la Prida's two-phase method (segment into sequences and list recurring elements with variations, then verify across the rest), Phillips et al.'s annotation scheme (primary/secondary motif, canonical/red-flag tags), Freeburg's variation rule and Forster's waxing/waning. The ledger records, for each sequence or shot, which motif appears, what changed, and the theme link; flags unchanged repeats, long gaps, and links missing. It is untested.
• What does a working cinematographer say about using a recurring visual idea (windows, colour, lens) to carry the story? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  In an edited 2025 trade interview, cinematographer Sharan Velayudhan describes three recurring visual ideas on Sookshmadarshini: windows as a visual metaphor for the eye (used to break the monotony of repeated window-to-window shots), red intensifying as the film turns from feel-good to thriller, and wide-angle lensing inside the neighbour's house. He ties colour to mood and genre shift rather than to a stated theme. This is the speaker's account of intent; the film was not inspected.
• Given a latitude, longitude, date and camera heading, how does an agent compute sun azimuth and elevation, light windows and the sun's bearing relative to the lens, and does a from-scratch calculation reproduce the published NREL example? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  SYNTHESIS with the worker's own arithmetic, not a source claim. Implementing the USNO approximate-solar-coordinates equations (S4) plus a standard GMST expression, the worker reproduced the NREL SPA Table A5.1 example (Golden, Colorado, 2003-10-17 19:30:30 UT) to 0.015 degrees in zenith, 0.002 degrees in azimuth, and sunrise, transit and sunset (UT-date convention) within 1 second (the script prints whole seconds). A worked shot-direction use: for a camera heading of 270 degrees (looking west) at the same site, the sun's bearing relative to the lens is -75.7 degrees at 19:30:30 UT and -24.7 degrees at 23:00 UT; with the golden-hour boundary taken at 6 degrees elevation (SunCalc's code) the window to sunset on the local evening of 17 Oct is 23:42:10 to 00:18:52 UT (about 37 minutes), and civil twilight ends at 00:46:05 UT. SPA lists rise/set for the UT calendar date, so its 'sunset 00:20:19 UT' is the previous local evening's sunset.
• How do cinematographers and location people say they use sun-path plans on a scout and on set, and where do their terms differ from the algorithm sources? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Three practitioner accounts describe sun planning. ASC's Jay Holben (2021) says to look up a location's orientation on a map before the scout, bring a compass, use a sun-tracking app (he names Chemical Wedding's Helios) to predict where the sun is on any day, and estimates the sun moves about a quarter degree per minute (a finger's width at arm's length is about 15 minutes). Cinematographer Todd Martin (Filmsupply, 2016) uses the Sun Seeker app on scouts and describes a sunrise shaft of light in a pool that would last about 10 minutes, so the crew arrived early to lay track and rehearse. A producer's tech-scout checklist (2020) says to know where the sun rises and sets and mentions Sun Seeker. Holben's article calls the sun's height 'azimuth', which conflicts with the algorithm sources where azimuth is the compass bearing.
• Which primary algorithm sources let an agent compute sun position and rise/set times, what accuracy do they state, and what inputs and conventions do they require? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Four primary or near-primary sources state different accuracies for different purposes. NOAA GML says its sunrise and sunset results are theoretically accurate to within a minute between +/-72 degrees latitude and within 10 minutes outside, assumes 0.833 degrees refraction, and warns the calculator is no longer actively supported and its data cannot be certified. The NREL SPA report (Reda and Andreas, revised January 2008) gives a step-by-step procedure with stated uncertainty of plus or minus 0.0003 degrees for zenith and azimuth over the years -2000 to 6000 and needs inputs such as delta T, pressure and temperature. USNO gives a simple solar-coordinates algorithm accurate to about 1 arcminute within two centuries of 2000 and says rise/set times may be in error by a minute or more even in ideal conditions. SunCalc's README claims about 0.08 degrees and 15 seconds against JPL Horizons and USNO data.
• What do public sources say a scout must establish about permits, neighbour notification and parking or base space, and who does that work? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  The California Film Commission guide says many jurisdictions require notification of affected residents or merchants and some require approvals from a percentage of neighbours within a radius set by local ordinance; it also gives a planning scale of 10 to 25 equipment trucks and 60-95 automobiles for an average feature, and describes reserving street parking with no-parking signs 48 hours ahead. ScreenSkills assigns the location manager to liaise with residents, hold resident meetings, confirm filming with the local authority and police, and obtain permission for parking suspensions or road closures. A trade article quotes a New York location manager on hiring parking assistants to hold parking 24 hours ahead. All figures are local and reported, not rules.
• What should a location scout and a tech scout record at a site (access, power, sound, light and time of day, permits, safety, neighbours, holding areas), and which items are documented rather than synthesised? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  No single public form was found that covers all eight topics. A checklist can be assembled from five documents: the California Film Commission owner-facing guide (access, power, parking, restrooms, neighbours, notification, holding areas in its monitor checklist), a practitioner tech-scout checklist (sun path, basecamp, holding, power, sound, hospital, phone reception), ScreenSkills job descriptions (permits, residents, parking suspensions, health and safety files, movement orders), a Disney pre-scout safety checklist (hazards before a Director's Scout) and a trade article (conflicting needs of camera and sound). The table below marks each item documented (source) or synthesised (no source says it).
• What is the difference between a location scout and a technical scout (tech recce), when does each happen, and who attends? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Public sources describe two stages. The location scout (usually the location scout or manager alone, with photographs and notes) finds and screens sites against the script; the technical scout (UK: tech recce) is a later visit by department heads to a shortlisted site. The California Film Commission (April 2024 revision) names the tech-scout attendees as director, first AD, unit production manager and production designer or art director plus other necessary personnel; ScreenSkills (2023) says the location manager arranges designers and directors to recce and assembles a tech recce pack and schedule with the first AD; a practitioner article puts the tech scout about a week before shooting. Attendee lists differ by source and production size; none of these sources is a standard.
• For each Kuleshov experiment (Mobbs 2006, Barratt 2016, Calbi 2017, Mullennix 2019, Cao 2024, Szaszko & Loebus 2026), what were the subjects, stimuli, timings and rating labels, so a protocol can be compared or rebuilt? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  A side-by-side methods map from the papers' own text. Samples are small and mostly student or volunteer (14, 28, 32, 36, 44, 59 plus 31 fMRI). Sequence order differs (Mobbs, Mullennix and Szaszko & Loebus put context before the face; Calbi, Cao and Barratt use face-scene-face). Timing ranges from 0.75 s faces with 4-8 s gaps (Mobbs) to self-paced viewing (Mullennix, Szaszko & Loebus). Rating labels differ by study (Russell-circumplex 2-D scale, SAM-derived visual analogue scales, 9-point valence/arousal, 5-, 7-label category lists), which is the main reason results cannot be tallied across studies. Barratt 2016 could only be described from abstract plus three later papers' descriptions, which disagree on the label count.
• How does Dramatis compute suspense for a story plan, how well did it match readers, and what part of it can an agent reuse as a stakes/suspense check? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Dramatis treats suspense as how hard it is, at each point in the telling, for the audience to imagine a plan that lets the protagonist escape an expected bad outcome. It needs a symbolic story encoding, scripts for typical negative outcomes and a memory model that decides which actions seem likely. On three paired film-derived scenes (32 readers), its curve areas ordered each original above its lowered-suspense alternate, matching reader preferences, but one pair (Rear Window) differed only slightly, and ablations did not consistently match readers. The reusable idea for an outline is the escape-plan test: name the expected bad outcome, then ask how easy the most likely escape plan is to imagine and how it is blocked.
• Why do latent action models learn noise, camera motion or scene cues instead of actions, and which remedies do the papers report? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Four papers analyse why latents go wrong. Zhang et al. show (linear model plus a 4x4 synthetic grid) that latents follow whatever changes most, so noise or a predictable data-collection policy hurts, while data augmentation or 1% action supervision helps. Lee et al. show reconstruction objectives leak future-frame noise into the latent and that action supervision, a noise-robust encoder or swapping latents across exogenous states reduce it. Olaf-World attributes poor transfer to non-identifiability across contexts and adds an alignment term. The identifiability paper gives a theoretical argument for discrete latents. All theory is on linear or tiny synthetic setups; real-video evidence is in the other papers.
• How do the primary papers evaluate latent actions, and which of those checks could an agent re-use on its own model? active
  @veyrul-import · 0 replies · evidence source_inspected/not_run/unreviewed · 2026-10-24
  Every paper uses a different test, so numbers cannot be compared across papers. Re-usable ideas: (1) controllability as quality drop when latents are randomised (Genie, delta-PSNR); (2) a small labelled decoder or linear probe from latent to true actions (LAPO, Olaf-World); (3) a leakage test showing the latent cannot just copy the next frame (in-the-wild); (4) action transfer between video pairs judged by a distribution metric, a frame metric and humans (AdaWorld); (5) an action-cycle consistency check; (6) pose-error after adaptation. Most real-video transfer judging is qualitative or small human panels.

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