{"id":"be429379-0cf1-4d04-abe4-3a470892d863","arxiv_id":"2412.16677","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":1,"one_line_summary":"A two-stage storyboard-to-video framework is claimed to outperform existing models on VBench, but the evidence is not independently verifiable and the reported tables are inconsistent.","lead":"This paper presents a two-stage text-to-video system: first a model turns text into storyboards of poses and layouts, then a diffusion transformer turns the storyboards into video. The authors report top VBench scores, but provide no code, data, or evaluation details, and the tables contain inconsistencies.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The SOTA claim rests entirely on self-reported VBench numbers, and the printed tables are internally inconsistent (rank inversion, mismatched aggregates); no code, checkpoints, or evaluation protocol is provided to resolve it.","rationale":"The reader's weakest assumption is exactly the load-bearing point: the reported VBench scores are the only evidence for the headline, and they are neither externally verifiable nor internally consistent. My reading agrees with that assessment. I did not find a separate methodological flaw in the two-stage storyboard idea; that idea is coherent and the paper honestly lists limitations in §6. However, an empirical SOTA claim without code, weights, or a stated evaluation protocol cannot support acceptance. The rank inversion in Table 1 and the unexplained gaps between per-dimension averages and reported aggregates are concrete, checkable defects that shift the burden to the authors. Since the reader already recommended REJECT and my concern does not change that recommendation, the verdict should remain unchanged.","tokens_in":8196,"tokens_out":9091,"duration_ms":78975,"concrete_test":"Obtain the VAST checkpoint and exact VBench evaluation configuration from the authors, then run the official VBench pipeline and compare all 16 per-dimension scores, aggregates, and ranking to Tables 1–3. Also re-sort Table 1 by total score and recompute the aggregates from Tables 2–3 under VBench's published weighting; if the Gen-3/Data-Juicer inversion or the 88.60-vs-88.98 discrepancy persists, the claimed SOTA result is not reproducible from the paper.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim — VAST outperforms existing methods and sets a new standard (§1, §4.1) — is an empirical claim supported only by the self-reported VBench comparison in Tables 1–3. That support is not checkable as submitted. The paper gives no model weights, evaluation code, prompt list, frame counts, seeds, or per-dimension sample sizes. Worse, the printed numbers contain internal defects: Table 1 ranks Gen-3 (83.32) at rank 9 below Data-Juicer (82.53) at rank 8, and Section 1 reports a quality score of 88.60 while Table 1 reports 88.98. The per-dimension tables also do not transparently produce the claimed aggregates: for VAST, the seven quality dimensions in Table 2 average 90.30 and the nine semantic dimensions in Table 3 average 72.88, whereas Table 1 lists 88.98 and 92.63; no aggregation rule or per-dimension weights are stated. Because every headline statement is derived from these numbers, the load-bearing assumption — that the scores were produced by VAST under the official VBench protocol and are correctly aggregated — is unsupported. A plausible storyboard idea does not substitute for missing evidence; the empirical contribution is currently unverifiable.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes VAST, a two-stage text-to-video generation framework. In the first stage, StoryForge converts a textual prompt into a storyboard consisting of human poses and object layouts using a causal multimodal LLM and learned autoencoders; in the second stage, VisionForge uses a Diffusion Transformer (DiT) extended with a Storyboard Adapter Module to generate video conditioned on the storyboard and text. The authors claim that this decoupling of text understanding from video generation improves temporal consistency, motion dynamics, and semantic accuracy, and they report state-of-the-art VBench scores: total 89.71, quality 88.98, and semantic 92.63 (Tables 1–3). The paper does not provide code, model checkpoints, evaluation scripts, or a detailed evaluation protocol, and the reported numbers contain internal inconsistencies.","tokens_in":8446,"tokens_out":4721,"duration_ms":41657,"significance":"If the reported results were verified and artifacts were released, the storyboard-based intermediate representation would be a plausible and potentially valuable contribution to controllable video generation, particularly given the claimed margin of 11 points in semantic expression over the next-best method. The paper also names concrete strengths: a two-stage design that separates scene structure inference from video synthesis, and a large curated dataset. However, as submitted, the empirical claim is unverifiable: the manuscript supplies no code, checkpoints, or evaluation details, and its own tables do not add up consistently. The technical architecture is described only at a high level, with no ablations or statistical analysis. Therefore, the significance of the contribution cannot currently be assessed from the submitted evidence.","major_comments":[{"comment":"The ranking in Table 1 is internally inconsistent: Gen-3 has a total score of 83.32 but is assigned rank 9, while Data-Juicer has a lower total score of 82.53 and is assigned rank 8. If the rows are intended to be sorted by total score, the ranks are wrong; if some other ordering is used, it must be explained. As printed, the table does not support claim that VAST 'achieves the highest overall score' relative to a correctly ordered list.","section":"Section 4.1, Table 1"},{"comment":"The Introduction (Section 1) reports that VAST achieves a video quality score of 88.60%, while Table 1 lists the same quantity as 88.98. These values should be identical. The discrepancy indicates that the reported numbers have not been carefully cross-checked, and it is impossible to determine which value is correct, further eroding confidence in the leaderboard comparison.","section":"Section 1 vs. Table 1"},{"comment":"The per-dimension scores in Tables 2 and 3 do not aggregate to the totals shown in Table 1. For VAST, the seven quality dimensions in Table 2 average to approximately 90.30, not 88.98, and the nine semantic dimensions in Table 3 average to approximately 72.88, not 92.63. No aggregation rule, per-dimension weights, or sample sizes are stated. As a result, the headline total and sub-scores cannot be reproduced from the data provided, which directly undermines the central claim of state-of-the-art performance.","section":"Section 4.1, Tables 2 and 3"},{"comment":"The evaluation protocol is not described. The paper does not specify the number of prompts per VBench dimension, the inference settings (sampling steps, guidance scale, resolution, video duration), random seeds, or the exact version of the VBench benchmark. It also provides no error bars or statistical tests. Without this information and without access to code, model checkpoints, or evaluation scripts, the reported VBench scores cannot be independently verified, so the statement that VAST 'outperforms existing methods in both visual quality and semantic expression' is not supportable as submitted.","section":"Sections 3 and 4"},{"comment":"The paper presents no ablation study. Because the entire motivation of the work is that the two-stage storyboard decomposition improves video quality and consistency (Section 1), an ablation is needed to attribute the reported performance to the storyboard mechanism. For example, one would need to compare VAST against a single-stage text-to-video model using identical data and compute, or against VisionForge trained with ground-truth versus generated storyboards. Without such evidence, the causal role of the proposed intermediate representation is unverified.","section":"Section 4.1 (overall)"}],"minor_comments":[{"comment":"The text says 'As shown in Fig. 3' when describing the StoryForge architecture, but Figure 3 is the VisionForge diagram; the StoryForge diagram appears to be Figure 2. This makes the description of the framework confusing.","section":"Section 3.2"},{"comment":"The caption contains the stray token 'final_moe_best', which appears to be a leftover from an internal experimental pipeline. It should be removed.","section":"Figure 4 caption"},{"comment":"The Storyboard Adapter Module is named but not described in any technical detail; its architecture, layer counts, input fusion method, and the manner in which its output is merged into the DiT backbone are not given, which hampers reproducibility.","section":"Section 3.3"},{"comment":"The dataset of 100 million images and 30 million video clips is described in a single sentence without details on the annotation pipeline, filtering criteria, or data sources, and the autoencoder training losses are only named, not defined.","section":"Section 3.1"},{"comment":"The Introduction refers to 'human movement' while Table 3 lists the corresponding dimension as 'Human Action'; the terminology should be made consistent.","section":"Section 1 and Table 3"},{"comment":"The phrase 'these examples highlight V AST's ability to intricate action sequences' is missing a verb; it should read 'ability to generate intricate action sequences' or similar.","section":"Figure 5 caption"}],"recommendation":"reject","confidential_remarks":"The manuscript's central claim is an empirical SOTA claim supported only by self-reported VBench numbers, and the paper provides neither artifacts nor a reproducible evaluation protocol. The internal inconsistencies in Tables 1–3 additionally suggest the numbers were not verified. I believe the paper would need a major overhaul—including releasing code/checkpoints, correcting the tables, and adding ablations—before it could be considered for publication. As submitted, it does not meet the standard of evidence required for the claims made."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The core idea here is genuinely worth a look: decouple text-to-video into a storyboard stage (pose + layout via a causal MLLM) and a storyboard-to-video stage (DiT with a storyboard adapter). That is a sensible architectural move, and the paper gives a clean, readable description of the two-stage pipeline, the training schedule, and the dataset scale. The storyboard representation is a plausible mechanism for improving temporal consistency and semantic control. I would credit the authors with identifying a real problem and proposing a coherent, if not deeply novel, solution.\n\nBut the empirical claim is the whole ballgame, and it does not hold up as submitted. The paper's headline — SOTA on VBench, \"new standard\" — rests entirely on self-reported numbers in Tables 1–3. No code, weights, evaluation scripts, seeds, or prompt lists are provided. That alone would be a major weakness for a benchmark-claims-only paper. Worse, the printed numbers are internally inconsistent: Table 1 ranks Gen-3 (83.32) at rank 9 below Data-Juicer (82.53) at rank 8; the abstract says quality score is 88.60 while Table 1 says 88.98; and the per-dimension tables do not aggregate to the claimed totals (Table 2's seven quality dimensions average about 90.30, not 88.98; Table 3's nine semantic dimensions average about 72.88, not 92.63). No aggregation rule or weights are given. These are not nitpicks; they are red flags that the central evidence is unreliable as printed.\n\nThe stress-test note is right on the money here. I don't see a way to rescue the SOTA claim from the artifact as it stands. The idea could still be good, and the qualitative figures suggest the system does produce coherent, dynamic videos. But a \"we built it and it works better than everyone\" paper needs either open artifacts or at least self-consistent, auditable numbers.\n\nBottom line: the paper is not ready for publication in its current form. The storyboard framework deserves a serious referee if the authors supply code and corrected, reproducible evaluations — the architecture is worth engaging with. But the version in front of us is a desk reject: the load-bearing evidence is missing and the numbers contradict themselves. If the authors release artifacts and fix the tables, I'd be happy to read the revision.","headline":"Storyboard-as-intermediate-representation is a reasonable idea, but the paper's SOTA claim rests entirely on internally inconsistent, self-reported VBench numbers with no code or evaluation artifacts to back them.","tokens_in":8975,"tokens_out":611,"would_cite":false,"duration_ms":6757,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"By generating a storyboard before the video, the two-stage VAST framework claims the top VBench score of 89.71, with perfect marks on object classification and human action.","keywords":["video generation","text-to-video","storyboard representation","temporal consistency","diffusion transformer","controllable generation","VBench benchmark"],"falsifier":"Run the official VBench evaluation code on the VAST model (if released) with identical prompts and sampling parameters; if the reproduced overall score falls materially below 89.71 or the per-metric rankings change, the claim of state-of-the-art performance is falsified.","tokens_in":7998,"feed_emoji":"🎬","tokens_out":6603,"duration_ms":49933,"temperature":0.7,"pith_summary":"The paper proposes VAST, a two-stage framework for text-to-video generation that inserts a storyboard between the prompt and the video. StoryForge first converts the text into a structured storyboard of human poses and object layouts; VisionForge then renders a video from that storyboard using a diffusion transformer. The claim is that this decoupling of language understanding from video synthesis gives the model precise spatial and temporal control, improving motion, consistency, and semantic accuracy. On the VBench leaderboard the paper reports the top overall score of 89.71, with perfect 100 scores on object classification and human action, and high marks on temporal flickering, subject consistency, and background consistency. If the claim holds, storyboards are a practical way to make video generation both more controllable and more interpretable.","feed_headline":"Storyboard-first video generator hits 89.71 on VBench","feed_subtitle":"A storyboard-first pipeline scores top marks on both visual quality and semantic accuracy.","key_machinery":"The central mechanism is the storyboard, an intermediate representation that captures human poses and object layouts as explicit control signals. In StoryForge, pose and layout autoencoders compress these signals into latent spaces, and a causal multimodal large language model predicts the next pose and layout features from text and prior timesteps. In VisionForge, those features are fused with text via a Storyboard Adapter using self-attention layers, then merged into a DiT-based diffusion transformer that renders the video. The storyboard thus carries spatial and temporal constraints from language to pixels, which is what the paper credits for temporal consistency, motion dynamics, and semantic accuracy.","core_discovery":"The central discovery claimed is that introducing a storyboard as an explicit intermediate representation—a sequence of human poses and object layouts—between text and video substantially improves both visual quality and semantic fidelity. StoryForge, built on a causal multimodal LLM with pose and layout autoencoders, predicts latent pose and layout sequences that capture spatiotemporal structure. VisionForge, a DiT-based diffusion model with a storyboard adapter, fuses those features with text to synthesize video. The reported results on VBench show VAST achieving an overall score of 89.71, quality score of 88.98, and semantic score of 92.63, including perfect scores on object classification and human action, with high marks on subject and background consistency and temporal flickering. The paper interprets these numbers as evidence that decoupling text understanding from video generation through storyboard control sets a new standard for dynamic and coherent video synthesis.","pith_inferences":["A fair independent re-run of VBench with the official code (or a released checkpoint) would test whether the reported 11-point semantic margin over the next-best method reflects a genuine advantage or a protocol difference in prompt selection and sampling.","The modular split suggests a practical route to user control: editing the storyboard—moving a bounding box or changing a pose—should translate directly to edited video content, something one-stage models cannot offer without retraining.","If the storyboard representation generalizes, a similar intermediate structure could be applied to image-to-video, 3D generation, or world-model training, where explicit layout and pose priors are also valuable."],"forward_implications":["The two-stage design means each stage can be trained and improved separately, so gains in storyboard prediction or storyboard-to-video rendering can be developed independently.","Because the storyboard explicitly encodes poses and object layouts, users gain fine-grained control over subject dynamics and scene composition without per-video fine-tuning.","The near-perfect scores on temporal flickering, subject consistency, and background consistency indicate that the storyboard largely resolves inter-frame stability for the tested prompts.","High semantic scores, including 100 on human action and 97.02 on spatial relationships, suggest the framework aligns generated content with textual descriptions more faithfully than one-stage baselines.","The principle of using an interpretable intermediate structure for generative tasks could carry over to other settings, such as longer narratives or multi-shot generation where cross-shot continuity is a known failure mode."],"supporting_citations":[{"why":"Provides the VBench benchmark and leaderboard metrics that all quantitative claims are measured against.","marker":"[7]"},{"why":"Introduces the Diffusion Transformer backbone that VisionForge extends.","marker":"[11]"},{"why":"Supplies the scaled rectified-flow transformer architecture used as the base for VisionForge.","marker":"[4]"},{"why":"The causal multimodal LLM that StoryForge uses to predict pose and layout latent sequences.","marker":"[3]"},{"why":"The DiT model that VisionForge merges storyboard features into.","marker":"[12]"},{"why":"A large-scale text-to-video diffusion transformer baseline that VAST claims to outperform.","marker":"[16]"},{"why":"A strong text-to-video baseline included in the VBench comparison.","marker":"[8]"},{"why":"A recent text-to-video model included in the VBench comparison.","marker":"[9]"}],"fun_headline_variants":["Storyboard-first video model tops VBench with 89.71","Text-to-video via storyboards: better control, higher VBench","VAST: storyboard intermediate boosts video generation scores","Two-stage video gen: storyboards then diffusion hits 89.71"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The reported VBench scores in the tables were produced by the described VAST system following the official protocol and are directly comparable to the listed competitor scores.","fun_headline_variants_meta":{"raw":{"variants":["Storyboard-first video model tops VBench with 89.71","Text-to-video via storyboards: better control, higher VBench","VAST: storyboard intermediate boosts video generation scores","Two-stage video gen: storyboards then diffusion hits 89.71"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000426,"raw_usage":{"total_tokens":2138,"prompt_tokens":857,"completion_tokens":1281,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":473,"completion_tokens_details":{"reasoning_tokens":1207}},"tokens_in":473,"tokens_out":1281,"duration_ms":8700,"temperature":1.0,"reasoning_tokens":1207,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T10:21:30.424613+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the official VBench evaluation code on the VAST model (if released) with identical prompts and sampling parameters; if the reproduced overall score falls materially below 89.71 or the per-metric rankings change, the claim of state-of-the-art performance is falsified.","supporting_citations":[{"cited_title":"Scaling recti- fied flow transformers for high-resolution image synthesis","cited_arxiv_id":null,"evidence_quote":"Supplies the scaled rectified-flow transformer architecture used as the base for VisionForge."},{"cited_title":"Vbench: Comprehensive bench- mark suite for video generative models","cited_arxiv_id":null,"evidence_quote":"Provides the VBench benchmark and leaderboard metrics that all quantitative claims are measured against."}],"review_version":1}