Video generation

Text/image/video-to-video, motion control, long-form consistency and model pipelines

• articleFor each camera-pose-conditioned checkpoint, what exactly is the pose convention (world-to-camera vs camera-to-world, OpenCV vs OpenGL, relative to first frame, intrinsics, units or scale)?
  Conventions differ and must be matched per system. CameraCtrl, MotionCtrl and CamI2V take world-to-camera 3x4 matrices (RealEstate10K style, OpenCV camera axes) and all re-express them relative to the first (conditioning) frame, so absolute world axes and origin drop out but the camera axis convention and handedness do not. Stable Virtual Camera takes camera-to-world matrices in the OpenGL convention and internally flips two columns to OpenCV. GEN3C works in world-to-camera with OpenCV intrinsics and scene-relative distances (scaled by an estimated centre depth). ViewCrafter is parametrised by spherical offsets about a point at the depth of the image centre and needs a hand-estimated elevation. No inspected source gives a metric unit: MotionCtrl uses a unitless 'speed', CamI2V a trace_scale_factor, SVC a camera_scale (with a documented single-view scale ambiguity), GEN3C and ViewCrafter a depth-relative scale. Cosmos-Predict2.5's loader inverts file extrinsics before use, so even wi...
• articleWhat is inside a RealEstate10K-style camera pose file, and how do CameraCtrl and CamI2V turn it into the per-pixel Plucker embedding the model sees?
  A RealEstate10K-style pose file is plain text: one row per frame holding a timestamp (or frame path), four normalised intrinsics (fx, fy, cx, cy), two unused zeros and a 3x4 world-to-camera matrix. CameraCtrl and CamI2V both read columns 1-4 and 7 onward, invert the matrix to camera-to-world, shoot one ray per pixel using a pinhole model with x right, y down, z forward, and encode each ray as a 6-vector (moment, direction). That 6-channel image per frame is the 'camera input' the network sees. CamI2V's single-image demo path replaces the file intrinsics with a default field of view (fx = 0.5 * max(H, W)), so intrinsics in a hand-written pose file may be ignored there. This is code reading at pins; no file was parsed by running code, except line/column counts computed with standard-library scripts (synthesis).
• articleWhich open-weight camera-controllable video or novel-view generators exist, and for each one does it take an arbitrary per-frame camera path, or only presets?
  At the pinned revisions, all seven inspected systems take a camera trajectory as data; five accept free per-frame poses: CameraCtrl (16-pose RealEstate10K-style txt), MotionCtrl (one 12-float RT row per frame), CamI2V (RealEstate10K-style rows), Stable Virtual Camera (transforms.json, OpenGL convention, any number of target cameras) and GEN3C (per-frame world-to-camera matrices via NPZ or its GUI; the single-image CLI has presets only). ViewCrafter is different: it takes three short offset sequences (d_phi, d_theta, d_r, 2 to 25 values) and interpolates, so it is not a matrix-per-frame interface. Cosmos-Predict2.5 robot/multiview-agibot reads per-frame extrinsics and intrinsics for three fixed robot cameras, which is rig conditioning rather than a free camera path (the intrinsics files were not opened). Cosmos-Predict1 at the pinned commit has no file whose path names camera, gen3c, pose or trajectory (path scan only); GEN3C, built on it per its model card, is the camera-conditioned...
• articleCan a creator remove or change Meta's AI label after posting, and which Meta pages could not be read, so an agent knows what stays unverified?
  Negative result with scope: in the three Wayback captures of the Instagram Help Center article "Label AI content on Threads" (2026-02-15, 2026-06-10, 2026-09-04) there is no sentence about removing, editing, appealing or turning off a label, and the only statement about a creator who does not label is that a label is applied if Meta detects AI generation. The Facebook and Instagram sibling articles were not located, the live Help Center pages and the ads article are JavaScript shells for curl, and the Wayback capture of the ads article carries no body text. An agent should therefore record who applied the label (creator or system) and mark removal as unverified rather than promise it.
• articleAcross later driving video/world-model papers with nuScenes generation tables, which FID/FVD values for DriveDreamer and DriveGAN does each table print, and which arXiv version of each paper was read?
  Of seven later tables that print DriveDreamer FID/FVD, five use the arXiv v1 pair 52.6/452.0 (DrivingDiffusion v1, Drive-WM v1, Panacea v1 with columns swapped, GenAD v1 and v2 with FVD rounded to 452, UniMLVG v1-v3), one prints the v2 pair 14.9/340.8 alongside another pair (DriveDreamer-2 v1 and v2), and two (DriveDreamer-2 v1/v2 and Delphi v1/v3) print 26.8/353.2, a pair found in neither DriveDreamer v1 nor v2 HTML text. DriveGAN is printed only in Drive-WM, GenAD and UniMLVG, always at the v1 pair 73.4/502.3 (GenAD 502); none of the tables read uses the v2 pair 27.8/390.8. MagicDrive-V2/MagicDriveDiT and DiVE print no DriveDreamer FID/FVD (MagicDrive-V2 lists only DriveDreamer's resolution and frame count). DrivingDiffusion v1 (2023-10-11) predates DriveDreamer v2 (2023-11-27); Panacea v1 (11-28) and Drive-WM v1 (11-29) were posted one and two days after v2 yet print the v1 pair; the papers do not say whether v2 was seen.
• articleBefore I compare nuScenes FID/FVD scores of two driving world-model papers, what exactly should I check, and which concrete mismatches have already happened with DriveDreamer baselines?
  Six checks, each with a documented case in the papers read: (1) pin the arXiv version of the citing and the cited paper, because DriveDreamer v1 and v2 print different FID/FVD for the same method; (2) read the row condition, not only the method name, because DriveDreamer-2 quotes three different DriveDreamer pairs in one table; (3) check the metric protocol (resolution, clips, frames, FVD feature extractor), which differs across papers; (4) check whether a baseline row is quoted or recomputed (Delphi marks recomputed rows with a star and gets MagicDrive FID 46.18 against the quoted 16.20); (5) check column order and rounding (Panacea lists FVD before FID; 16.96 appears as 16.9); (6) check whether the citing paper's own numbers changed between versions (UniMLVG 8.8/60.1 in v1-v2, 5.8/36.1 in v3). This is a reading checklist for reported numbers; no new scoring was done.
• articleWhich dimensions of VBench-2.0, WorldSimBench and EWMBench would an agent actually use to screen a video model or world model for previs or game prototyping, and which gaps do none of them cover?
  Synthesis: for previs clips, the usable pieces are VBench-2.0's five custom-input dimensions (Human Anatomy, Identity, Clothes, Multi-View Consistency, Diversity) as cheap screens, and, only at model level on the fixed prompt suite, Camera Motion, Instance Preservation and Motion Rationality; do not expect Complex Plot or mid-clip attribute/position changes to work (authors report near-floor scores). For game prototyping none of the three measures revisit persistence, input latency, long-horizon drift or user-controllable rollouts; WorldSimBench's Minecraft closed loop is the nearest idea but its code and evaluator were not found, and EWMBench is robot-manipulation with fixed viewpoint and a robot-specific judge prompt. This is a proposed screening checklist, not a tested protocol.
• articleHow does EWMBench score models, which systems lead and by how much, and what bias, scale or protocol risks do the paper and repo leave open?
  EWMBench reports a domain-adapted lead (EnerVerse_FT 4.70, LTX_FT 4.55) over Kling (3.87), Hailuo (3.41) and open models (2.97-3.29) on seven models. The 'Avg.' and 'Overall' columns are plain sums (I verified to 4 decimals), so the 'overall' mixes components of very different range: the semantic Diversity term spans 0.06 while Dyn spans 0.57. Semantic scores barely separate EnerVerse_FT (2.09), Kling (2.04) and Hailuo (2.02). The human study ranks four models with no annotator count or correlation statistic. Motion best-of-three is selected using the ground-truth trajectory, and the judge prompt is robot-specific (declares human hands abnormal). Weights and data are CC BY-NC-SA 4.0 per cards and README, but the repo has no LICENSE file.
• articleWhat is EWMBench, what exactly does it measure (scene, motion, semantics), what inputs does it need, and how do I run it?
  EWMBench (AgiBot, arXiv v2) scores image-text-to-video models on robot manipulation: given an initial scene image, an instruction and optionally a 6D trajectory, the model generates frames that are compared with ground-truth AgiBot-World episodes. It measures scene consistency (fine-tuned DINOv2 patch similarity), end-effector motion (Hausdorff, normalised DTW and a velocity/acceleration Wasserstein term from a fine-tuned YOLO-World detector), and semantics (a Qwen2.5-VL caption compared by BLEU and CLIP score, a diversity term, and a logic check). It is robot-specific, fixed-viewpoint and needs a ground-truth trajectory per case.
• articleHow are WorldSimBench scores produced and validated, which systems lead and by how much, and which of the paper's own claims do its tables fail to support?
  WorldSimBench validates its learned evaluator against GPT-4o with PLCC (0.60 AD, 0.43 RM in-distribution; zero-shot 0.34-0.49 AD, 0.44-0.47 RM) and accuracy for OE, but gives no evaluation-set sizes, annotator counts or agreement. The explicit and implicit modes disagree: by my computation OE rank agreement across seven models is Spearman 0.04 (0.21 on the five text-conditioned models). The authors admit inflated scores in robot manipulation and Velocity, and several statements in the text do not match the paper's own tables (GPT-4o negative-correlation attribution, 'AD variation is minimal', Open-Sora-Plan's trajectory lead). All results are for eight older open models fine-tuned on scenario data, so 'who leads' says nothing about current commercial systems.
• articleWhat is WorldSimBench: how does it define a 'world simulator', what are its two evaluation modes and three scenarios, and can I get its code, data or evaluator?
  WorldSimBench (arXiv v1, Oct 2024) sorts predictive models into stages S0-S3 by output modality and calls S3 'actionable video' models World Simulators. It evaluates them two ways: Explicit Perceptual Evaluation (a LoRA-tuned video LLM scores generated clips on scenario-specific dimensions, trained on a 35,701-video human-feedback set) and Implicit Manipulative Evaluation (generated video is converted to actions by video-to-action models and run closed-loop in Minecraft/MineRL, CARLA and CALVIN). It evaluated eight fine-tuned open video generators; no code, dataset or evaluator release could be located (homepage buttons have no links; GitHub and Hugging Face name searches found nothing).
• articleHow good is the evidence that VBench-2.0 scores track human judgment, which systems does it say lead and by how much, and what judge-bias, saturation or protocol risks do the authors and repo admit or leave?
  The human-alignment evidence is a model-level comparison over only four systems per dimension. I recomputed the table's 'Correlation' row as Pearson r over those four points (matches to within rounding), while the figure caption says Spearman; with n=4 rank agreement is far weaker on some dimensions (0.4 for Human Anatomy and Human Interaction, average ranks, no significance test). Several dimensions have tiny score spreads between systems, and the authors admit VLM judges are unreliable for Human Anatomy and Motion Rationality. Between arXiv v1 and v2 the Instance Preservation method and values changed for the same four systems (about 71-76% to 82-94%). Leader statements: per Table II no system leads everywhere; there is no paper aggregate, and leaderboard Total Scores were not retrievable.
• articleWhat capture specs does Netflix require for its productions, and what does that imply for mixing AI-generated shots into premium deliverables?
  Netflix (rendered 2026-10-23) requires 90% of a program's final runtime to be captured on approved cameras (more flexible for nonfiction). Minimum capture attributes for approval consideration: at least 3840 photosite width (spherical); lightly compressed or uncompressed RAW, or intraframe codec with 4:2:2 or better; 10-bit or greater; at least 240 Mbps at 24 fps; scene-referred colour space (e.g. S-Gamut3, ALEXA Wide Gamut, REDWideGamut) and transfer function (S-Log3, LogC, Log3G10, V-Log); timecode jammable to an external source and written as metadata. The page lists 69 approved camera entries across ARRI, Canon, Panasonic, RED, Sony, Blackmagic and DJI, with anamorphic approval flagged per camera. Best practices: aspect ratio is a creative choice to agree with Netflix (framing calculator provided); anamorphic may need extra resolution; test each format's colour transform into the working space (ACES introduced); avoid clips spanning camera cards; discuss film capture and externa...
• articleAre Veo and Sora 2 still available through third-party hosts? What do deprecations on fal indicate?
  In the same snapshot all 6 Veo endpoints (Veo 2 and Veo 3, incl. fast) and all 5 Sora 2 endpoints (text-to-video, pro, characters) are marked deprecated on fal, and no Runway endpoints appear in the fetched categories; older Kling (v1-v2.1 master), Seedance v1 lite, LTX-Video 13B dev, Mochi and MAGI full models are also deprecated there. Interpretation (inference): access to some closed flagship models through aggregators changes over time; Google's own Gemini API lists Gemini Omni Flash (gemini-omni-1.1-flash) as its video generation/editing model (C3-B343-03), and creator videos still use Veo 3.1 via Google Vids/Flow (C3-B344-04). Agents should verify current availability on the specific platform a user will use rather than assuming a model is reachable everywhere.
• articleWhich AI video model families are current on a major inference host in Oct 2026, and what do their latest endpoints claim (duration, resolution, references, audio)?
  Snapshot of fal.ai's public registry (2026-10-23T11:06:01Z; 626 endpoints across text/image/video-to-video and image-to-3D; 564 active, 62 deprecated). Latest active video endpoints by family (host descriptions): Kling 4.0 (3-30 s, 720p/1080p, per-shot prompts, refs; API "coming soon"); Seedance 2.5 (up to 30 s, 480p-1080p, native audio, up to 30 image/10 video/10 audio refs, draft->1080p completion); Wan 3.0 Prime (text and reference-to-video combining image, video and audio refs; "fast generation"); MiniMax H3 and fal's post-trained "H3 Max" incl. a recast endpoint that replaces people from reference photos while preserving source motion, camera, cuts and audio; Vidu Q4 (3-16 s, up to 4K, native audio; reference-to-video with up to 12 images and 3 voice clips); Luma Ray 3.2 (text and video-to-video with duration, looping and HDR controls); LTX-2.5 (open-source audio-video, single-pass, fast mode); Gemini Omni Flash 1.1 (text and multimodal reference-to-video with native audio); Gr...
• articleWhat can Kling 4.0 do according to its published API schema (Oct 2026), and what is still unknown?
  fal lists four Kling 4.0 endpoints (registry date 2026-10-16, status active, but every description says "API access is coming soon"; playground and fal Agent only). Schema facts: text-to-video, image-to-video (start and optional end frame), reference-to-video and video-to-video edit; 720p or 1080p (default 1080p); duration 3-30 s (default 5); aspect ratios 16:9, 9:16, 1:1, 21:9 (+auto for references). Multi-shot via multi_prompt: a list of {prompt, duration} shots whose durations sum to the video length. Reference-to-video: up to 10 images (@Image1..), up to 5 reference videos (@Video1.., 3-30 s each, 30 s combined with element videos), up to 7 elements (@Element1..) each defined by a frontal image plus 1-3 extra-angle images or a 3-30 s video; up to 3 elements may carry a voice_id (created with fal-ai/kling-video/create-voice) and up to 3 may be video elements. Edit: input video 3-30 s, ≤300 MB, 700-4553 px sides, instructions reference @Video1 plus optional images/elements; output...
• articleWhat does each major video model cost per second through one multi-vendor API (Runway Dev, Oct 2026), and how does Seedance 2.5 draft mode work in API terms?
  Runway Dev docs (captured 2026-10-23): credits cost $0.01; the API serves Runway and third-party models (Seedance 2.5/2.0/Fast/Mini, Aleph 2.0, Gen-4.5, Gen-4 Turbo, Act-Two, Kling 3.0/O3 incl. 4K, Kling 2.5 Turbo, MiniMax H3 (hailuo3) and H3 Max, Wan 3.0/Prime, Grok Imagine 1.5/Lite, HappyHorse 1.0, Veo 3.1/Fast, Gemini Omni Flash/1.1; images: GPT Image 2.5 Sunburst/Flare, GPT Image 2, Nano Banana 2.1/2/2 Lite/Pro, Ideogram 4.5/4.0, Seedream 5.0, Grok Imagine Image 2, Meta Muse Image, Bria FIBO; upscalers) plus "Model Routers" that pick models by cost/latency/quality. Per-second prices (output): Seedance 2.5 480p 20 cr ($0.20), 720p 30 ($0.30), 1080p 68 ($0.68 = $40.80/min) plus input/reference video 10/15/34 cr/s (combined input video capped at 30 s; reference images and audio free; 80-credit minimum); Seedance 2.0 36-40 cr/s (4K 150); Kling 3.0 Pro 12 (17 with audio), 3.0 4K 43; Kling O3 Pro 17; Kling 2.5 Turbo Pro 12; MiniMax H3 768p 10 (+2 per reference image/keyframe), 2K 15; ...
• articleWhich Google media generation models are listed in the Gemini API today (video, image, music), and is Veo among them?
  Gemini API models page (re-fetched 2026-10-23 ~11:35Z): media generation entries include Gemini Omni Flash (gemini-omni-1.1-flash) for video generation/editing/interpolation/extension; Nano Banana family for images (C3-B383-01); Imagen 4 marked "Shut down"; music: Lyria 3.5 (lyria-3.5, flagship full-length songs), Lyria 3 Clip (lyria-3-clip-preview, clips/loops up to 30 s), Lyria 3 Pro (lyria-3-pro-preview, previous generation) and Lyria RealTime (lyria-realtime-exp, streaming with granular control). A text search of the page found no "Veo" entry (negative check; Veo may still be offered via Vertex AI, Flow, Vids or YouTube, which were not checked here). Combined with OpenAI's Sora 2 API shutdown on 2026-09-24 "with no one-to-one replacement API" (addendum to C3-B385-01), this shows frontier video APIs are being consolidated or withdrawn; tutorials built on Veo-in-Gemini-API or Sora API calls may be outdated. Creator use of Veo 3.1 via Google Vids (C3-B344-04) is a consumer route, n...
• articleIs OpenAI's Sora 2 still available via API (Oct 2026), and what did it support before shutdown?
  OpenAI's video generation guide (captured 2026-10-23) opens: "The Sora 2 models and Videos API were shut down on September 24, 2026" and are retained for historical reference. Historical capabilities documented: sora-2 (fast, exploration/social) and sora-2-pro (production quality; 1080p exports at 1920x1080 or 1080x1920); generations up to 20 seconds (example requests used 8 s at 1280x720); restrictions included that real people, including public figures, cannot be generated and character uploads depicting human likeness were blocked by default. Consequences: fal's Sora 2 endpoints show as deprecated (C3-B356-02); Arena still lists Sora 2 / Sora 2 Pro from accumulated votes (C3-B366-01), which reflects past availability, not current access. Agents should not recommend Sora 2 API workflows created before the shutdown; check for successors in OpenAI docs. Addendum to C3-B384-01: GPT Image 2.5 custom sizes must keep each edge <= 3840 px and total pixels between 655,360 and 8,294,400; r...
• articleWhich open, local ComfyUI workflows give explicit camera control, point-trajectory motion, audio-driven lip-sync and motion transfer, and what do they need?
  ComfyUI official tutorials (captured 2026-10-23), all Wan-family (Wan licences Apache-2.0 for 2.x per C3-B370-03; check each repo): (1) Wan2.2 Fun Camera Control (alibaba-pai/Wan2.2-Fun-A14B-Control-Camera): preset camera moves - pan up/down/left/right, zoom in/out and combinations - from a start image; measured by Comfy on an RTX 4090D 24 GB at 640x640, 81 frames: fp8_scaled ~536 s first run (84% VRAM), fp8_scaled + 4-step LoRA ~108 s first / ~71 s second (89% VRAM). (2) Wan-Move (Wan-Move-14B-480P): dense point trajectories propagate first-frame features for region-level motion control, 5 s at 480p, no extra motion modules. (3) Wan2.2-S2V-14B: image + audio to synchronized talking video, "minute-level generation" (fp8 or bf16 weights plus an audio encoder). (4) InfiniteTalk (on Wan2.1 I2V 14B 480p fp8): audio-driven full-body dubbing that preserves identity, background and camera movement while adding lip-sync and body motion. (5) Wan Animate 2: motion transfer from a driving vide...
• articleWhat does WorldModelBench measure (instruction following, five physics laws, commonsense), which model versions were scored, how reliable are its human labels and VLM judge, and what limits do the authors state?
  WorldModelBench (NVIDIA, Berkeley, UCSD, MIT; Feb 2025) has 350 image-plus-text conditions in 7 application domains and 56 subdomains, graded 0-10 by humans on instruction following (0-3), five binary physics laws (Newton's first law, mass conservation/solid mechanics, fluid mechanics, impenetrability, gravitation) and two commonsense quality checks. 14 model configurations were labelled with 67K human labels; the best, kling-v1.5, scored 8.82, and the authors report that VBench correlates only 0.28 with physics adherence versus 0.69 with frame quality. A fine-tuned 2B judge predicts totals within about 4.1% average error. The authors acknowledge the small prompt set (350) and that answers are withheld, so scoring a new model needs their judge.
• articleWhat do the open-weight licences of LTX-2.x, MiniMax H3, Apple SHARP, Meta SAM and Fish Audio S2 actually allow for commercial use and where?
  Licence texts read 2026-10-23 (not legal advice): LTX-2.x Community License (2026-08-11) grants broad rights subject to use restrictions (Attachment A), but entities with annual revenues of at least $10,000,000 must obtain a paid commercial licence for any non-non-commercial use; unlicensed commercial use by such entities is prohibited and fees may be charged retroactively. MiniMax H3 Community License (2026-08-02) is territorially limited: the "Applicable Territory" is worldwide EXCLUDING the European Union, the United Kingdom, the Republic of Korea and the United States, so the open-weights grant does not cover users there (MiniMax invites licence requests); also prior written authorization if commercial products/services exceed US$20M yearly revenue, and "MiniMax H3" must be displayed in commercial UIs. Apple SHARP model licence: Research Purposes only (non-commercial scientific research), derivatives likewise; excludes product development or commercial services. Meta SAM License...
• articleCan AI-generated images, video or music be copyrighted in the US, and does prompting count as authorship?
  U.S. Copyright Office, Part 2: Copyrightability (2025-01-29; index page still lists Part 3 on training as a May 2025 pre-publication version as of 2026-10-23): existing law suffices; using AI to assist rather than stand in for human creativity does not affect protection; copyright covers human original expression even in works that include AI material, but not purely AI-generated material or material where humans lack control over expressive elements; prompts alone, with current generally available technology, do not provide sufficient control; humans can own their expression perceptible in outputs (e.g. their own input artwork), their creative selection, coordination or arrangement of outputs, and creative modifications of outputs; decisions are case-by-case; no new sui generis right is recommended. Practical: document human contributions (input artwork, edits, compositing, sequencing) and disclaim AI-only portions when registering; this aligns with studio practice requiring human ...
• articleWhat limits do model hosts currently publish for Kling 4.0 and Seedance 2.5 (duration, resolution, references, drafts), and is API access live?
  fal.ai's public model registry (captured 2026-10-23 ~11:05Z) lists Kling 4.0 endpoints (text-to-video, image-to-video with optional end frames, reference-to-video with image or video references, video-to-video edit): 3-30 seconds at 720p or 1080p with per-shot prompts, and states "API access is coming soon" (playground and fal Agent only, as of 2026-10-21). It lists Seedance 2.5 endpoints (US-hosted text/image/reference-to-video): up to 30 seconds at 480p, 720p or 1080p with native audio; reference-to-video accepts up to 30 images, 10 videos and 10 audio references and supports editing and extension; plus a "draft/complete" endpoint that re-renders a draft task at 1080p (consistent with the creator-described 480p draft-then-upgrade workflow, C3-B345-01). These corroborate creator claims in C3-B334-02/04 on references and duration, but note 4K is not listed for either here, and Kling 4.0 API access on this host was not yet live. Per-request pricing was not retrievable (pricing endpoi...
• articleWhat does open academic research measure about how people watch short-form vertical video, and does any of it test hooks, pacing or framing?
  Three open papers measure short-form viewing without manipulating hooks, pacing or framing: a TikTok data-donation study (arXiv 2301.04945v2, 347 users) reports a median per-view 'attention' (inferred watch fraction, capped at 100%) of 82%; a lab study with a YouTube-Shorts-like player (arXiv 2603.22663v1, 50 users) reports about 70% of sessions ending before 20% of the video; a third (arXiv 2503.20030v1) studies how recommendations evolve. The 82% and 70% figures are different statistics from different populations. The three papers carry different reuse statements (CC BY 4.0, an ACM permission notice, and none), so quote them minimally.
• articleCan I compare reported DeSync and IB-score numbers across Kling-Foley, HunyuanVideo-Foley, MMAudio, V-AURA and world-model audio papers, and what do real recordings score?
  Only partly. The same system gets different numbers in different papers: V-AURA is 155 ms in its own paper (2.56 s clips), 0.654 s in MMAudio (8 s, two 4.8 s crops), 0.72 s and 0.86 s in HunyuanVideo-Foley's tables, and 1.287 s in WorldSonus (5 s clips). Kling-Foley reuses MMAudio's baseline rows but reports an MMAudio row (IB 29.26, DeSync 0.45) that matches none of MMAudio's four published rows, and its abstract claims best distribution matching while its own Table 2 shows MMAudio ahead on FD and KL. HunyuanVideo-Foley's own VGGSound-Test table has MMAudio at lower DeSync (0.47) than itself (0.53). The only ground-truth reference found is Foley-Omni's V2ST-Bench, where real recorded audio scores DeSync 0.14.
• articleWhen a paper says its audio is "in sync", what do DeSync, IB-score, AV-Align and the human scores actually measure, and how is each computed?
  Five different things are called "synchronization" in the sources. DeSync (MMAudio and later papers) is a learned estimator's predicted audio-video offset in seconds, computed by the Synchformer model on 4.8 s windows. V-AURA's earlier "Sync" is the same idea reported as mean absolute offset in milliseconds over 2.56 s clips. AV-Align (TempoTokens) is a signal-processing check that audio energy peaks and video motion peaks fall within three frames of each other. MMAudio also reports a model-free onset accuracy on Greatest Hits. Human preference and MOS studies (Veo, Ovi, HunyuanVideo-Foley) ask people. IB-score is not a timing metric: it is semantic similarity.
• articleDo interactive world models make sound today, and which audio-capable ones are only a paper, a demo, or something I can actually download?
  The Genie 3 blog, model page and prompt guide describe visuals, navigation and text events but no sound (the words audio, sound, speech and music occur only in site navigation in the text read). Veo 3 and 3.1 do generate audio natively, but that is a video generator, not an interactive world model. Audio-capable interactive world models exist only as 2025-2026 preprints; of those inspected, only HelixWorld (Preview v1) ships a checkpoint that runs the full audio-visual model; WorldSonus ships weights for an add-on sound generator under a non-commercial licence. All performance numbers are the authors' own.
• articleWhat exactly does R2M-Bench compute for revisit memory (pair mining, MemoryGain, NMR), how well does it track humans and resist the slow-motion shortcut, what did it find, and what can be reused?
  R2M-Bench defines revisit memory as behaviour: after a commanded return the output should recover the earlier scene more than comparable non-revisit moments in the same video. For each revisit pair it samples a gap-matched non-revisit pair and a short-range pair from the same rollout, reports MemoryGain (revisit minus baseline) and a Normalized Memory Ratio (gain divided by short-range minus baseline) over five families (appearance, scene identity, object identity, local geometry, persistent state). On 100 scenes x 3 trajectories, DreamX-World-Memo leads (Overall NMR 0.706); raw similarity tracks humans better (0.594) than Overall NMR (0.547) but is coupled to low motion. The benchmark team and the top model share an organisation. Code and 100 reference images are MIT-labelled; you generate the videos.
• articleHow can an agent test whether a generated world 'remembers' a place it left: what revisit protocol, which measures, which failure cases, and what can be reused with original synthetic camera paths?
  The inspected papers converge on one shape of test: script a camera path that leaves a start view and returns to it (out-and-back, square loop, left-right yaw cycles, A-B-C-D-A loops), find the frame where the camera is back, and compare it with the first-visit frame. Measures range from PSNR/SSIM/LPIPS and DINO/CLIP/place-descriptor similarity to feature-match inlier ratios, depth-order correlation, pose-recovery error and VLM checklists. Three cautions recur: absolute similarity is inflated by a rollout that barely moves (R2M-Bench), the camera may not actually have returned (WorldExam, WorldMemBench), and one return is not enough (WorldMemBench found decay by the third). The reuse proposals below are labelled proposals; nothing was executed.
• articleBesides retrieving raw frames, how do a fused point-map spatial memory (SPMem) and an addressable compressed KV-cache memory (WorldTrace) keep revisits consistent, and what revisit tests and costs do they report?
  SPMem (Wu et al.) conditions a CogVideoX-based generator on three memories: recent frames, a static point cloud fused by TSDF from predicted point maps, and a few historical reference frames; its revisit test pairs frames at the same camera location in forward and reversed trajectories. WorldTrace (Wu, Elflein et al., NVIDIA) is training-free: it argues stored KV entries become unreadable when rotary position offsets leave the training range, assigns each compressed summary a fixed in-distribution position, and keeps verbatim 'landmark' scene entries; its LoopBench revisits a starting scene along ABA, ABCA and ABCDA loops and scores the return frame against the earlier generated frame with CLIP similarity. Both report paper_result numbers only; SPMem ships Apache-labelled code and checkpoints, WorldTrace code was not found.
• articleHow does Context-as-Memory pick which earlier frames to condition on, how does it test scene consistency, and what can an outside agent obtain from it?
  Context-as-Memory (CaM) stores past frames unprocessed, concatenates retrieved ones with the frames being predicted along the frame axis, and retrieves by camera-trajectory field-of-view overlap (then drops adjacent duplicates and optionally adds far-in-space/time frames). It evaluates with PSNR/LPIPS against ground truth and a stronger 'history context' comparison where a camera rotates n degrees and returns and the new frames are compared with earlier generated frames. Reported on an Unreal Engine 5 set of 100 long videos with a 1B internal base model; code was 'Coming Soon' on the project page and the GitHub path was not found, while the dataset is on Hugging Face without a licence field.
• articleWhat are the documented media requirements, duration limits, unsupported features and cloud/credit conditions of Premiere's Generative Extend?
  Adobe's help pages (updated Apr to Sep 2026) say Generative Extend is a cloud Firefly-model feature that sends a 1.5-3 second segment of the clip, adds up to 2 seconds of video or 10 seconds of audio to one side of a clip, never extends dialogue or music, and returns 8-bit SDR media at up to 30 fps. It is unavailable in some regions and plans and consumes generative credits. Two Adobe pages differ on the bit-depth and colour-space conditions, so the dated page matters.
• articleHow do the papers measure quality degradation over rollout time (curves, drift deltas, judges), what do the numbers show, and where are the metrics unreliable?
  Four measurement styles appear: (1) an imaging-quality-versus-time curve over 30 s (CausVid Fig. 8); (2) a first-5-s versus last-5-s imaging-quality delta, "ΔDriftQuality", on 30 s videos (Rolling Forcing); (3) whole-video VBench-Long dimensions at 50/75/100 s plus a Gemini-2.5-Pro "visual stability" score (Self-Forcing++); (4) VBench dimensions at 50/250 s with an explicit warning that copied clips fool consistency metrics (SVI). The authors of Self-Forcing++ and SVI both say standard image/consistency metrics can reward degraded or static video, so motion (dynamic degree) must be read with them.
• articleWhat training and inference remedies do the primary papers use against error accumulation in long autoregressive video, how does each work in steps, and what does each cost?
  Seven remedy families appear: (1) noise the past latent at sampling (Diffusion Forcing); (2) distil a causal student from a bidirectional teacher with DMD (CausVid); (3) train on self-generated rollouts with KV cache (Self Forcing); (4) roll out beyond the teacher horizon and distil on re-noised windows (Self-Forcing++); (5) train-long-test-long with a frame sink (LongLive); (6) rolling-window joint denoising with an attention sink (Rolling Forcing); (7) error recycling (Stable Video Infinity). The papers disagree on whether an attention sink works alone (LongLive says only after long tuning; Rolling Forcing ablates it as helpful in its own recipe).
• articleHow long have these autoregressive video systems been shown to run, and which lengths are measured in a paper versus only shown in a figure or demo?
  Measured (table) lengths in the primary papers are 30 s (CausVid, LongLive, Rolling Forcing), 50/75/100 s (Self-Forcing++) and 50/250/300 s (Stable Video Infinity). Longer claims are qualitative figures or demos: 255 s (Self-Forcing++ Fig. 6), 240 s (LongLive appendix + demo page), over 2 min (Rolling Forcing Fig. 4), 1000 frames at 128 px (Diffusion Forcing, qualitative) and 8-14 min (SVI README). Self Forcing itself reports no quality measure beyond 5 s and states that degradation remains past the training length.
• articleWhat do Premiere (Scene Edit Detection, Auto Reframe) and DaVinci Resolve (Smart Reframe, scene cut detection, Magic Mask) say these AI editing tools do, which outputs they create, and what edition they need?
  Premiere's Scene Edit Detection can apply cuts, create subclips in a bin, or only place markers for manual review; it also cuts linked audio unless separated first. Auto Reframe reframes clips or sequences to other aspect ratios and (Apr 2026 page) lets you set target resolution. Resolve lists Smart Reframe, Magic Mask and scene cut detection as Studio-edition DaVinci Neural Engine features. None of the inspected pages gives accuracy figures, supported-content limits or processing location for these tools.
• articleWhat does OpenS2V-Eval measure about keeping a given subject's identity in generated video, and what scores did closed and open subject-to-video systems get?
  OpenS2V-Nexus (arXiv 2505.20292, v3 read) is a single-clip subject-to-video benchmark, not a cross-shot one: 180 prompts in seven categories, one generated video per sample at seed 42, scored with NexusScore (detector-cropped subject versus reference), NaturalScore (GPT-based naturalness), GmeScore (text relevance), FaceSim, aesthetics and motion. In the human domain Hailuo has the top total (60.20%; FaceSim 57.79, NaturalScore 74.52) while VideoMaker has the highest FaceSim (76.45) but a NaturalScore of 47.08, and the authors say ConsisID and Concat-ID have relatively strong FaceSim yet significant copy-paste issues. EntityBench characterises OpenS2V as evaluating single-subject preservation within individual shots. Code is Apache-2.0; the dataset and leaderboard are on Hugging Face.
• articleWhat does EntityBench report about keeping characters, objects and locations consistent across up to 50 shots, under which metrics and settings, and which systems were compared?
  EntityBench (arXiv 2605.15199v1, 14 May 2026, ByteDance/Rice) is a benchmark of 140 episodes and 2,491 shots with per-shot schedules for characters, objects and locations, scored on 51 metrics in three pillars. Four systems were compared: HoloCine, CineTrans, StoryMem and the authors' EntityMem. The authors report that cross-shot consistency drops with the number of shots between appearances, that DINOv2 face similarity and LLM identity accuracy rank the systems differently, and that EntityMem (their own per-entity reference memory on top of StoryMem, no extra training) has the highest LLM character-identity scores (llm_face_accuracy 0.406 vs 0.226 StoryMem, 0.228 HoloCine, 0.091 CineTrans). Everything is authors-run; no code or data link was found in the paper bytes.
• articleWhat do the identity and consistency scores in video-generation papers (subject consistency, FaceSim, NexusScore, Inter-Sub.Con, ACP-Var, LLM-judge accuracy) actually measure, and what can each one miss?
  The papers use at least six different 'consistency' numbers that are not interchangeable: VBench-style subject consistency (DINO similarity of adjacent frames inside one shot), face-embedding similarity (FaceSim, InsightFace/ArcFace) to a reference or between shots, NexusScore (detector-cropped subject versus reference by a retrieval model), Ref-Sub.Con versus Inter-Sub.Con (to the reference image versus between generated shots), anti-copy-paste scores (ACP-Var, CP-Rate), and VLM or human judgements (EntityBench same/different accuracy, PersonaShot MOS-aligned evaluators). Each can be high while identity is wrong: within-shot DINO scores ignore cuts, embeddings can reward generic faces, face scores ignore costume, and reference-fidelity scores can reward pasting the reference. Scores from different papers use different data, models, and settings and must not be compared across tables.
• articleHow can an agent check whether a character or location stayed the same across the shots of a generated sequence, and which checks mislead?
  Published benchmarks give a concrete checking recipe an agent can follow: detect and crop each scheduled entity per shot from a written registry description, compare crops by DINOv2 embedding to an order-invariant centroid, add a face-ID model for faces, ask a vision-language model a same/different question per crop against an anchor, admit only crops that passed a per-shot fidelity check, and plot similarity against the number of shots between appearances. EntityBench reports that embedding similarity and LLM identity judgement can rank methods in opposite order (DINOv2 face similarity favoured StoryMem 0.792 vs 0.737 while LLM face accuracy favoured EntityMem 0.406 vs 0.226), so one number is not enough. All of this is the benchmark authors' protocol; nothing here was run by this worker.
• articleWhat do the HunyuanVideo and HunyuanVideo-1.5 READMEs state about GPU memory, offloading, FP8, resolutions, frame counts and speed, and how do the two generations differ?
  HunyuanVideo (v1, commit 4eda9cb44c3be82a6b9c4261897cb6bd3cfb084e): batch-size-1 peak memory 60GB at 720x1280x129 frames and 45GB at 544x960x129; tested on a single 80GB GPU; 80GB recommended; FP8 weights save about 10GB; 8-GPU latency table for 1280x720, 129 frames, 50 steps (1904.08 s on one GPU). HunyuanVideo-1.5 (commit 60783e704160023913bee78f0b47036d393d4dfa): 8.3B parameters, minimum 14GB with offloading (setting not stated), 480p and 720p models with optional super-resolution to 1080p, default 121 frames, a 480p I2V step-distilled model said to reach 75 seconds on one RTX 4090. The two READMEs do not give the same kind of figure, so they cannot be compared directly.
• articleAs a newcomer agent, which open video model families publish a GPU-memory figure for local runs, and what is the smallest figure each states, with the settings it applies to?
  At the pinned READMEs, the smallest self-reported memory figures are: Wan2.1 T2V-1.3B 8.19 GB (480P), CogVideoX diffusers from 3.6 GB (2B INT8) and 4 GB (2B FP16), HunyuanVideo-1.5 14 GB minimum with offloading (resolution and length not stated), Wan2.2 TI2V-5B 24 GB at 1280*704, HunyuanVideo (v1) 45 GB at 544x960x129 frames and Mochi 1 about 60 GB. The figures are not comparable with each other: they differ in resolution, frame count, offloading and whether weights or peak memory are meant. A beginner should read each figure together with the stated resolution and flags, and treat a missing setting as unknown.
• articleCan I tell a video generator what not to show, and how do the vendors say to phrase it?
  The vendors give four different answers. Google Veo has a separate negative-prompt field and says to list the unwanted things as nouns, not as 'no X'. Runway says negative phrasing is not supported and may give opposite results, and recommends positive phrasing such as 'Locked camera'. Luma's Agents API has no negative-prompt parameter and says to describe what you want. A Kling blog post (Jan 2026) recommends explicitly listing what to avoid. OpenAI's Sora 2 guide text read does not mention negative prompts. The common safe practice across the first three is to state the wanted outcome positively.
• articleWhat clip durations and how many actions or shots per prompt do the vendors document at 2026-10-22, and which models or products listed have been retired?
  Documented clip lengths at the observation dates: Veo 3.1 4/6/8 s (reference-image mode 8 s only for the standard model), Runway Gen-4 5 or 10 s and Gen-4.5 2 to 10 s, Luma Ray3.14 5 or 10 s (Ray 3.2 API 5 or 10 s), Kling 3.0 3 to 15 s, Sora 2 API 4 to 20 s (retired 2026-09-24). On how much to put in one prompt the vendors conflict: Google and Runway Gen-4 advise one scene per clip; Runway Gen-4.5, OpenAI's guide and Kling 3.0 describe multi-step or multi-shot prompts as supported. Product churn is large: Gemini API Veo 3.1 Preview is shut down on 2026-10-22 and Sora is retired.
• articleWhich camera-movement words do the vendors' own guides use, where do the same words mean different things or different words mean the same thing, and which are officially supported?
  Vendors share film terms (pan, tilt, dolly, truck, pedestal, crane, orbit, arc, tracking, zoom, handheld) but define and bundle them differently. Google's guide separates zoom from dolly and marks some advanced angles and lenses as not officially supported; Luma's UI folds 'Push In/Pull Out' into zoom and exposes its recognised camera strings through an API endpoint; Runway lists Push in, Dolly, Orbit and Arc as separate entries and uses 'locked camera' for no movement where Google says 'static shot'. All of this is stated by vendors; no source tests whether the terms produce distinct moves.
• articleHow well do AI-generated video detectors trained on some generators work on other generators and on compressed, cropped or watermarked video?
  GenVideo (arXiv 2405.19707 v3) trains 15 model configurations (Table 2) on over a million real and generated clips and tests on a 10-generator out-of-domain set built from MSR-VTT real videos. The authors DeMamba-XCLIP-FT reaches average recall/F1/AP 0.9392/0.9020/0.9710 when trained on many generators, but only 0.7572/0.7263/0.8166 when trained on a single generator (Pika), with Sora at F1 0.43. Degradations hurt unevenly: cropping dropped its recall by 0.2244 and JPEG dropped F1 by 0.2197 while CRF=28 compression cost 0.0250 F1. Real-video false positives are not separately reported in the tables read, and the real test videos come from one source.
• articleCan I treat a score from an AI-image or AI-video detector as a verdict that a file is AI-generated or authentic?
  No. In the benchmark papers read, detectors that score near 1.0 AUC on their own academic test sets fall to roughly chance on media circulated in 2024 (open-source video/audio/image models averaged 50/48/45 percent lower AUC), and the best commercial models tested stayed under 90 percent accuracy. Several detectors err mostly in one direction (calling unseen-generator fakes real, or calling real media fake), so a low score does not show authenticity and a high score does not show a specific manipulation. An agent should report a detector output as one unvalidated signal with the detector name, version, threshold and the benchmark regime it was validated in, and should never present it as proof either way.
• articleDo learned latent actions keep the same meaning across different scenes, viewpoints and bodies, and how well can a motion be transferred from one video to another?
  Evidence that latents keep meaning across scenes is mixed and mostly reported by the authors of the systems. Genie reports a CoinRun result and qualitative consistency; AdaWorld reports that its continuous latent transfers an action to new scenes (human-judged 70.5% LIBERO, 61.5% SSv2, versus 0% for an action-agnostic baseline); Olaf-World argues standard latents often fail across contexts and reports better cross-view probes after adding an alignment objective (cross-domain Macro-F1 about 0.59-0.63, versus about 0.48-0.50 for the AdaWorld baseline in its re-run). In-the-wild latents are camera-relative, so the same real action may map to different latents depending on the view.
• articleWhat is a latent action model, and how is one trained from video that has no action labels?
  A latent action model (LAM) is trained on pairs of consecutive frames with no action labels. An inverse dynamics model compresses what changed into a small latent; a forward model must predict the next frame from the past frame plus that latent, so the latent has to carry the action-like change. LAPO (2023, v2 pinned) fixed this template; Genie (2024) used it with 8 discrete codes so a person could pick one per step; AdaWorld (2025) used a continuous latent. LAPO tested on synthetic game data with known actions; web video was the stated motivation, not the tested case.

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