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arxiv: 2604.21718 · v2 · submitted 2026-04-22 · 💻 cs.CV · cs.AI· cs.CL· cs.LG· cs.MM

Recognition: unknown

Building a Precise Video Language with Human-AI Oversight

Chancharik Mitra, Deva Ramanan, George Liu, Hewei Wang, Irene Pi, Isaac Li, Jiaxi Li, Ruojin Li, Ryan Rao, Shihang Zhu, Siyuan Cen, Yili Han, Yilun Du, Yuhan Huang, Yu Tong Tiffany Ling, Zhiqiu Lin

Authors on Pith no claims yet

Pith reviewed 2026-05-10 00:34 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.CLcs.LGcs.MM
keywords video captioninghuman-AI oversightstructured video specificationvideo language modelscritique-based supervisionvideo generationCHAI framework
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The pith

A structured video description language refined by human critique lets open models outperform closed-source systems like Gemini-3.1-Pro on precise captioning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper defines a fixed vocabulary of visual primitives to describe subjects, scenes, motion, spatial relations, and camera dynamics in video. It then introduces the CHAI process, in which an AI first writes a pre-caption from this vocabulary and trained experts critique and revise it into a more accurate post-caption. These revised captions and the critiques themselves supply training signals that improve an open model through supervised fine-tuning, preference optimization, and inference scaling. The resulting model produces higher-quality captions than closed models and can be used to fine-tune video generators so they follow prompts of several hundred words with tighter control over cinematic choices. A reader would care because most current video AI still omits or misstates fine details that matter for professional work.

Core claim

The central claim is that a precise, human-curated specification for video content combined with critique-based human-AI oversight produces captions accurate enough to train open-source models that surpass closed-source models such as Gemini-3.1-Pro, and that the same captions enable video generation models to follow detailed prompts with improved control over camera motion, angle, lens, focus, point of view, and framing.

What carries the argument

The CHAI framework: models generate pre-captions from a structured specification of hundreds of visual primitives, experts critique them to produce post-captions, and the resulting pairs plus preferences supply supervision for SFT, DPO, and inference-time scaling.

If this is right

  • Higher critique quality in precision, recall, and constructiveness produces correspondingly better captioning, reward modeling, and critique generation in the downstream models.
  • The same re-captioned professional footage can be used to fine-tune video generators for longer, more controllable prompts up to 400 words.
  • Open models can reach professional-level performance on video understanding and generation with only modest amounts of expert supervision.
  • The division of labor between model text generation and human verification improves annotation efficiency while raising final caption accuracy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same primitive-based specification and critique loop could be extended to audio tracks or 3D scene descriptions if equivalent primitives are defined.
  • Once the initial specification and critique pipeline is built, the cost of scaling high-quality video data may drop because most of the text generation is off-loaded to models.
  • Widespread adoption of this style of precise video language would make it easier to audit and correct generative video systems for unwanted cinematic biases or omissions.

Load-bearing premise

That the fixed set of visual primitives captures every necessary video detail without systematic omission or bias and that the experts' critiques are consistently accurate and transferable beyond the videos they reviewed.

What would settle it

Apply the trained model to a fresh collection of professional videos never seen during captioning or critique collection and measure whether human raters still judge its captions more precise and complete than those from Gemini-3.1-Pro.

Figures

Figures reproduced from arXiv: 2604.21718 by Chancharik Mitra, Deva Ramanan, George Liu, Hewei Wang, Irene Pi, Isaac Li, Jiaxi Li, Ruojin Li, Ryan Rao, Shihang Zhu, Siyuan Cen, Yili Han, Yilun Du, Yuhan Huang, Yu Tong Tiffany Ling, Zhiqiu Lin.

Figure 1
Figure 1. Figure 1: Recipe for precise video language. We present a recipe for producing high-quality video captions (blue, bottom row) and compare it with prior work (red, top row). (1) While prior video–text datasets lack a clear policy to teach annotators and models what and how to describe, we develop a structured specification with professional video creators to collect descriptions that support comprehensive video under… view at source ↗
Figure 2
Figure 2. Figure 2: Specification is crucial in video language. Prior datasets (red, left column) that lack a clear specification often misuse visual and motion terms, omit key information, and include subjective or emotional descriptions that distract from the observable video content. Over the course of a year, we worked (blue, right column) with more than 100 professional video creators, including filmmakers, cinematograph… view at source ↗
Figure 3
Figure 3. Figure 3: Oversight is crucial for quality annotations. Even with clear task guidelines, writing detailed video descriptions remains error-prone for both humans and models. Our human evaluations show that prior datasets (red, left column) suffer from recurring issues: human-written captions often contain typos, grammatical errors, awkward phrasing, and events described out of order; while model-written captions freq… view at source ↗
Figure 4
Figure 4. Figure 4: Critique quality matters for post-training. Prior work [53, 88] often collects critiques that are inaccurate (e.g., hallucinating information), incomplete (e.g., skipping mistakes), or non-constructive (e.g., noting errors without explaining how to fix them). Our curation framework instead requires each critique to directly guide the model in producing the final post-caption, forcing annotators to write cr… view at source ↗
Figure 5
Figure 5. Figure 5: Re-captioning improves video generation. After fine-tuning on large-scale videos re-captioned by our post-trained Qwen3-VL, Wan2.2 can better follow detailed prompts (up to 400 words) more faithfully and achieve finer control over camera mo￾tion and video cinematography, such as dolly zoom movements and isometric (2.5D) views. Evaluations in Appendix H confirm that this pipeline outperforms both zero-shot … view at source ↗
Figure 6
Figure 6. Figure 6: Errors in crowdsourced video captions. Crowdsourced captions often omit key details, producing overly brief descriptions, and lack the visual vocabulary needed to describe common cinematic or motion effects. For example, crowdworkers may confuse a high vantage viewpoint with a “bird’s-eye view” (which refers to a top-down angle), describe a regular building as a “circular tower” because a fisheye lens bend… view at source ↗
Figure 7
Figure 7. Figure 7: Examples of errors caused by lack of specification. Without clear rules, captions often suffer from (a) misuse of terminology, (b) omission of details, or (c) inclusion of subjective interpretations. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Examples of errors caused by lack of oversight. Even with detailed prompts, unchecked captions may contain (a) typos, grammar mistakes, or more serious issues like describing events out of temporal order, (b) hallucinations of non-existent objects or events, or (c) confusion of subtle visual details. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Examples of error cases in MSR-VTT [72] [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Examples of error cases in ActivityNet Captions [26] [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Examples of error cases in ShareGPT4Video [12]. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Examples of error cases in UltraVideo [73] [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Examples of error cases in VDC [7] [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Examples of error cases in Dream1K [61]. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Examples of error cases in PerceptionLM (PE-Video) [15] [PITH_FULL_IMAGE:figures/full_fig_p020_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Examples of error cases in TUNA-Bench [25]. camera motion, or spatial layout. – Oversight: Captions were collected from thousands of crowdsourced AMT workers, with each clip annotated by several independent annotators. The only quality control mentioned is removing duplicate or very short sentences during post-processing. – Issues identified: Crowdsourced captions suffer from severe quality issues. Many d… view at source ↗
Figure 17
Figure 17. Figure 17: Demographics of video content creators [PITH_FULL_IMAGE:figures/full_fig_p024_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Overview of our specification. As the arrows indicate, motion and spatial captions depend on the completed subject and scene captions; Appendix J provides the full prompts detailing this dependency. provides an appeal workflow where annotators can request a regrade by emailing the reviewer and manager. We show the human-AI team structure in [PITH_FULL_IMAGE:figures/full_fig_p024_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Example screenshots from our captioning platform. (a) shows how annotators (or reviewers) use our platform to generate and critique model pre-captions, and (b) shows how they continue polishing the critique and post-caption until satisfied. (c) shows how new annotators can perform training by first completing the above steps, then comparing their critique with expert versions verified by multiple paper au… view at source ↗
Figure 20
Figure 20. Figure 20: Human-AI team structure [PITH_FULL_IMAGE:figures/full_fig_p026_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Word clouds of captions. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Subject caption examples. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Scene caption examples. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Motion caption examples. 30 [PITH_FULL_IMAGE:figures/full_fig_p030_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Spatial caption examples. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Camera caption examples. 32 [PITH_FULL_IMAGE:figures/full_fig_p032_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Comparing reference-based metrics. We evaluate each reference-based metric by measuring its pairwise accuracy (with tie optimization [18]) when comparing scores between any two captions in the evaluation set, which includes model pre-captions rated with human 1-to-5 Likert scores and adversarial negative captions that automatically receive a score of 1. LLM-Judge-Instruct with GPT-4o achieves the best per… view at source ↗
Figure 28
Figure 28. Figure 28: Comparing reward models using VQAScore versus text generation. We find that VQAScore (a logit-based probability score) significantly outperforms asking the model to directly output a Likert-scale score from 1 to 5 after supervised fine-tuning (SFT). Therefore, we stick to VQAScore for reward modeling in this work. 44 [PITH_FULL_IMAGE:figures/full_fig_p044_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Genre distribution for ∼150k curated videos. beam-search–like process). These extensions are left for future work. H. Video Generation In this section, we report statistics of the ∼150K professional videos we curated from YouTube, summarize their genre distribution, show additional qualitative comparisons with zero-shot Wan, and include a human study on prompt following using 200 prompts generated from ou… view at source ↗
Figure 30
Figure 30. Figure 30: Video generation example: dolly-zoom out [PITH_FULL_IMAGE:figures/full_fig_p049_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: Video generation example: isometric (2.5D) game perspective. 49 [PITH_FULL_IMAGE:figures/full_fig_p049_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: Video generation example: clockwise roll [PITH_FULL_IMAGE:figures/full_fig_p050_32.png] view at source ↗
Figure 33
Figure 33. Figure 33: Video generation example: Dutch angle. 50 [PITH_FULL_IMAGE:figures/full_fig_p050_33.png] view at source ↗
Figure 34
Figure 34. Figure 34: Video generation example: rack focus [PITH_FULL_IMAGE:figures/full_fig_p051_34.png] view at source ↗
Figure 35
Figure 35. Figure 35: Video generation example: speed ramp. 51 [PITH_FULL_IMAGE:figures/full_fig_p051_35.png] view at source ↗
Figure 36
Figure 36. Figure 36: Video generation example: side-view game perspective [PITH_FULL_IMAGE:figures/full_fig_p052_36.png] view at source ↗
Figure 37
Figure 37. Figure 37: Video generation example: watermark addition. 52 [PITH_FULL_IMAGE:figures/full_fig_p052_37.png] view at source ↗
Figure 38
Figure 38. Figure 38: Video generation example: height change from underwater to above water [PITH_FULL_IMAGE:figures/full_fig_p053_38.png] view at source ↗
Figure 39
Figure 39. Figure 39: Video generation example: shot size change from medium to close-up. 53 [PITH_FULL_IMAGE:figures/full_fig_p053_39.png] view at source ↗
Figure 40
Figure 40. Figure 40: Video generation example: revealing shot. 54 [PITH_FULL_IMAGE:figures/full_fig_p054_40.png] view at source ↗
read the original abstract

Video-language models (VLMs) learn to reason about the dynamic visual world through natural language. We introduce a suite of open datasets, benchmarks, and recipes for scalable oversight that enable precise video captioning. First, we define a structured specification for describing subjects, scenes, motion, spatial, and camera dynamics, grounded by hundreds of carefully defined visual primitives developed with professional video creators such as filmmakers. Next, to curate high-quality captions, we introduce CHAI (Critique-based Human-AI Oversight), a framework where trained experts critique and revise model-generated pre-captions into improved post-captions. This division of labor improves annotation accuracy and efficiency by offloading text generation to models, allowing humans to better focus on verification. Additionally, these critiques and preferences between pre- and post-captions provide rich supervision for improving open-source models (Qwen3-VL) on caption generation, reward modeling, and critique generation through SFT, DPO, and inference-time scaling. Our ablations show that critique quality in precision, recall, and constructiveness, ensured by our oversight framework, directly governs downstream performance. With modest expert supervision, the resulting model outperforms closed-source models such as Gemini-3.1-Pro. Finally, we apply our approach to re-caption large-scale professional videos (e.g., films, commercials, games) and fine-tune video generation models such as Wan to better follow detailed prompts of up to 400 words, achieving finer control over cinematography including camera motion, angle, lens, focus, point of view, and framing. Our results show that precise specification and human-AI oversight are key to professional-level video understanding and generation. Data and code are available on our project page: https://linzhiqiu.github.io/papers/chai/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper introduces a structured specification for video descriptions grounded in hundreds of visual primitives developed with professional filmmakers, along with the CHAI (Critique-based Human-AI Oversight) framework. In CHAI, experts critique and revise model-generated pre-captions into higher-quality post-captions; the resulting preference data is used for SFT and DPO training of open VLMs (e.g., Qwen3-VL) on captioning, reward modeling, and critique generation. Ablations link critique quality (precision, recall, constructiveness) to downstream gains. The approach is applied to re-caption professional videos and fine-tune video generation models (e.g., Wan) for detailed 400-word prompts controlling cinematography. The central claim is that precise specification plus modest human-AI oversight enables professional-level video understanding and generation, with the resulting open model outperforming Gemini-3.1-Pro.

Significance. If the empirical claims hold under rigorous evaluation, the work is significant for providing open datasets, benchmarks, and reproducible recipes that demonstrate scalable oversight can yield open models competitive with or superior to closed frontier systems. The release of data and code, the involvement of professional video creators in defining primitives, and the explicit separation of generation from verification in CHAI are strengths that support reproducibility and broader adoption in video-language research.

major comments (3)
  1. Experimental results section: the claim that the final model outperforms Gemini-3.1-Pro is central to the paper's contribution, yet the manuscript provides no quantitative metrics (e.g., human preference scores, automatic metrics, or exact evaluation protocol), dataset splits, or statistical tests in the main text or tables, preventing verification of the outperformance and its attribution to CHAI rather than other factors such as data volume or base model choice.
  2. Ablations subsection: the statement that critique quality in precision, recall, and constructiveness 'directly governs' downstream performance is load-bearing for the oversight framework's value, but the experiments do not appear to isolate this variable from confounds such as total annotation time, expert training, or the specific pre-caption model used; a controlled comparison holding other factors fixed is required.
  3. Video generation fine-tuning section: the application to Wan for 400-word prompts claims finer control over camera motion, angle, lens, etc., but no quantitative metrics (e.g., user study win rates or automated cinematography adherence scores) versus the base Wan model or other baselines are reported, weakening the claim that the approach transfers to generation.
minor comments (3)
  1. Abstract: the expansion of CHAI is given only in the body; repeating the full name on first use in the abstract would improve readability.
  2. Notation: the manuscript uses 'pre-captions' and 'post-captions' without a dedicated definition table or figure illustrating the CHAI loop; adding such a diagram would clarify the division of labor.
  3. References: several standard video captioning benchmarks (e.g., ActivityNet Captions, MSR-VTT) are mentioned but not compared against in the evaluation; adding a brief related-work discussion would situate the new datasets.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the careful and constructive review. The comments identify important areas where the manuscript can be strengthened for clarity and rigor. We address each major comment point-by-point below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: Experimental results section: the claim that the final model outperforms Gemini-3.1-Pro is central to the paper's contribution, yet the manuscript provides no quantitative metrics (e.g., human preference scores, automatic metrics, or exact evaluation protocol), dataset splits, or statistical tests in the main text or tables, preventing verification of the outperformance and its attribution to CHAI rather than other factors such as data volume or base model choice.

    Authors: We agree that the main text should contain the supporting quantitative evidence rather than relying primarily on the appendix and project page. We will revise the experimental results section to include a consolidated table with human preference win rates, automatic metrics (CIDEr, SPICE), the precise evaluation protocol (including rater instructions, number of raters, and inter-annotator agreement), dataset splits, and statistical tests. This will make the attribution to the CHAI framework more transparent by also showing the base-model ablation. revision: yes

  2. Referee: Ablations subsection: the statement that critique quality in precision, recall, and constructiveness 'directly governs' downstream performance is load-bearing for the oversight framework's value, but the experiments do not appear to isolate this variable from confounds such as total annotation time, expert training, or the specific pre-caption model used; a controlled comparison holding other factors fixed is required.

    Authors: We acknowledge the need for tighter isolation. While our existing ablations already hold annotation time roughly constant and use the same pre-caption model, we will add a new controlled experiment that fixes the expert pool, time budget, and pre-caption source while systematically varying only critique quality (via graduated training levels and simulated low-quality critiques). The revised ablations subsection will present these results to more convincingly demonstrate the causal link. revision: yes

  3. Referee: Video generation fine-tuning section: the application to Wan for 400-word prompts claims finer control over camera motion, angle, lens, etc., but no quantitative metrics (e.g., user study win rates or automated cinematography adherence scores) versus the base Wan model or other baselines are reported, weakening the claim that the approach transfers to generation.

    Authors: We agree that quantitative evidence is required to support the transfer claim. We will expand the video generation section with results from a user study measuring adherence to specific cinematographic elements and automated parsing-based scores for prompt compliance. These metrics, together with comparisons against the base Wan model, will be added to the main text. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents an empirical pipeline: a structured visual-primitive specification developed with external professionals, the CHAI human-AI critique loop for data curation, standard SFT/DPO training on the resulting preference data, and direct comparisons to external closed-source models (Gemini-3.1-Pro). Ablations measure critique quality against downstream gains via held-out evaluation rather than fitting parameters to the target metric. No equations, self-definitional loops, fitted-input predictions, or load-bearing self-citations appear in the provided description; the central claims rest on external benchmarks and human-verified data rather than reducing to the inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The paper presents an empirical framework and dataset curation method rather than a mathematical derivation; no free parameters, axioms, or invented physical entities are introduced.

invented entities (1)
  • CHAI (Critique-based Human-AI Oversight) framework no independent evidence
    purpose: To curate high-quality video captions by having experts critique and revise AI-generated pre-captions
    Newly proposed method in this paper for dividing labor between model generation and human verification.

pith-pipeline@v0.9.0 · 5687 in / 1266 out tokens · 36079 ms · 2026-05-10T00:34:48.278647+00:00 · methodology

discussion (0)

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