DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation

Chao Wu; Mushui Liu; Weijie He; Yunlong Yu; Zhao Wang

REVIEW 4 major objections 5 minor 45 references

DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read DyST-XL claims that a training-free combination of LLM-driven layout planning and frame-aware attention masking can give an off-the-shelf diffusion-transformer video model improved compositional control, lifting T2V-CompBench…

desk verdict A plausible training-free recipe for compositional T2V whose headline numbers are not yet evidenced: all comparisons rest on borrowed baselines from [29] with no matched re-run, no seeds, and no error bars. read the letter →

arxiv 2504.15032 v2 pith:FPOWRNIE submitted 2025-04-21 cs.CV

Weijie He , Mushui Liu , Yunlong Yu , Zhao Wang , Chao Wu This is my paper · ORCID

classification cs.CV

keywords compositionaltext-to-videogenerationtraining-freeinferenceattentionmaskingdynamiclayoutplanningentityconsistencydiffusiontransformerLLMpromptparsingT2V-CompBench

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that a training-free wrapper around an existing text-to-video diffusion model can repair the main failure modes of compositional generation—attribute confusion, spatial misplacement, entity identity drift, and implausible motion—without retraining or manual annotation. The wrapper asks a large language model to parse the prompt into entities, plan their start and end bounding boxes, and rewrite the prompt into a global caption plus per-entity prompts; intermediate boxes are linearly interpolated. During denoising, a frame-aware attention mask binds each entity's text tokens to video tokens inside its own moving box, and first-frame features inside each box are propagated forward to keep identity stable. On the T2V-CompBench benchmark, the paper reports consistent-attribute accuracy rising from 61.64% for the base model to 86.96%, spatial accuracy reaching 61.10%, and action accuracy reaching 73.21%. If the result holds, compositional video control can be added to off-the-shelf open models at inference time.

What carries the argument

The load-bearing mechanism is a frame-aware attention mask inside the diffusion transformer's cross-attention layers, whose coordinates come from LLM-planned dynamic bounding boxes. The mask has three parts: text-to-text masking restricts each entity's attribute tokens to attend only within their own semantic class, preventing color and motion leakage between entities; text-to-video masking binds an entity's prompt tokens to video tokens inside its per-frame bounding box; video-to-video masking lets tokens within a box attend freely and merges entity classes when their boxes overlap, so occlusions and interactions are modeled rather than cut off. The companion Entity-Consistency Constraint then copies the first-frame latent tokens inside each box as reference features and fuses them into the same entity's later-frame boxes at every denoising step, counteracting flicker introduced by hard masks. The dynamic boxes themselves are produced by the LLM planner for static and movable entities, with intermediate frames obtained by linear interpolation between keyframe layouts, so the entire pipeline reduces compositional control to a question of mask geometry and feature propagation.

What would settle it

Run the pipeline on the same 200 prompts per task with the LLM's planned boxes replaced by randomly jittered or pairwise-permuted boxes of identical shape. If the consistent-attribute and spatial scores stay near the reported 86.96% and 61.10%, then the attention-mask geometry—not the layout planning—carries the result; if they collapse, the LLM boxes are the load-bearing ingredient.

Watch

Extended reading notes

Core claim

DyST-XL's central claim is that the quality of complex, multi-entity text-to-video generation is mainly a matter of where and when each entity's text tokens may attend to video tokens, and that this can be regulated at inference time. The authors build on CogVideoX-5B and add three components: a Dynamic Layout Planner in which an LLM extracts entity-attribute pairs and produces physics-aware keyframe boxes with linear interpolation for motion; a Dual-Prompt Controlled Attention Mechanism that masks text-to-text, text-to-video, and video-to-video attention so that each entity's caption only influences its own region, with overlapping regions merged into interaction classes; and an Entity-Consistency Constraint that takes first-frame latent features from each entity's box and injects them into that entity's later-frame boxes during every denoising step. The paper reports that this configuration lifts CogVideoX-5B's consistent-attribute score on T2V-CompBench from 61.64% to 86.96%, spatial from 51.72% to 61.10%, and action from 53.33% to 73.21%, with ablation showing that each module adds a measurable gain.

Load-bearing premise

The entire approach assumes that the LLM's automatically planned bounding boxes are spatially accurate enough to serve as hard masking gates during denoising; a misplaced, missing, or implausibly moving box would either erase an object's textual influence or bind its text to the wrong region, and the paper provides no validation of layout accuracy or fallback when planning fails.

Editorial extensions

If this is right

Any DiT-based text-to-video model can gain compositional control at inference time: the same LLM-planning plus masked-attention recipe transfers to other off-the-shelf checkpoints without retraining.
Entity identity stays stable across frames because first-frame features, not per-frame re-estimates, anchor later frames for each entity.
Overlapping and interacting entities are handled by merging their mask classes, which the paper shows beats models that treat entities in isolation.
The improvements are not confined to attributes: spatial, action, and motion metrics all rise on T2V-CompBench, indicating the mask guides layout as well as appearance.
Because no training is involved, the framework can be updated rapidly when the base model improves, and it works in resource-constrained settings.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural stress test is to hold the mask geometry fixed but scramble the LLM boxes; if scores barely move, the attention mask alone—not the layout planner—is the active ingredient, which would change the scientific claim.
The method's ceiling is bounded by the LLM's spatial reasoning; prompts with unusual spatial verbs or many interacting objects are plausible failure clusters, though the paper does not break down errors by prompt type.
The same bounding-box-as-mask idea could apply to image DiTs or to camera-motion control, since nothing in the mechanism is video-specific except the frame interpolation of boxes.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a coherent training-free framework for compositional text-to-video, but the empirical claim is under-supported. The specific combination — LLM keyframe layout planning with linear interpolation, dual-prompt attention masking, and first-frame feature propagation — applied to CogVideoX-5B is new, and the qualitative examples are genuinely suggestive. The ablation shows a sensible monotonic progression on the three tasks reported, and the attention heatmap is a nice sanity check. The code release is a concrete step toward reproducibility.

Where it gets soft: Section 4.2 says all competitor results come from [29], and Table 2's baseline is the same 61.64 as Table 1, so the incremental gains from GP/EP/ECC are not controlled comparisons — they are measured against a number produced elsewhere, likely with different prompt template, resolution, seeds, and evaluation code. A 25-point gain over an unmeasured baseline cannot be trusted. No variance, no seed count, single-run point estimates. The Dynamic-attr improvement is 0.02 percentage points, the paper's sentence there is garbled, and the text says the baseline scored 25.32% while Table 1 says 61.64% — a reporting inconsistency that should have been caught. Only three of seven tasks are ablated, so we do not know how the components affect Motion, Action, Interaction, or Numeracy. The LLM layout planner is load-bearing: if the boxes are wrong, hard attention masks will bind text to the wrong regions, and there is no analysis of parse or layout failure rates. The citation for Regional Prompt FLUX ([41]) points to a different paper, which is sloppy.

Who it is for: people working on training-free control of DiT video models. The mechanism is worth discussing, but the evidence as reported does not support the state-of-the-art claim. If the code actually runs and the authors add matched baselines, seeds, and error bars, this could become a useful paper.

Recommendation: send it to peer review — the idea is coherent and the qualitative results are suggestive enough to warrant referee time, but it will need heavy revision.

Referee Report

4 major / 5 minor

Summary. The paper proposes DyST-XL, a training-free inference-time framework that wraps CogVideoX-5B with three components: an LLM-based dynamic layout planner that parses prompts into entity-attribute graphs and generates bounding-box trajectories, a dual-prompt controlled attention mechanism that applies frame-aware attention masks, and an entity-consistency constraint that propagates first-frame reference features to later frames during denoising. The method is evaluated on T2V-CompBench, where Table 1 reports large gains over the CogVideoX-5B baseline (e.g., Consist-attr 61.64% to 86.96%, Action 53.33% to 73.21%), with all competitor and baseline numbers imported from the benchmark paper [29]. Code is released at a GitHub link.

Significance. If the claimed gains survive a controlled evaluation, DyST-XL would be a practically significant contribution: it offers a training-free route to compositional control for an off-the-shelf DiT video model, with a clear high-level mechanism and an evaluation on a standard benchmark. The component decomposition is useful, and releasing the code is a strength. However, the current evidence base is too weak to support the headline claims: the baseline is not re-run under the authors' protocol, no variance or seed counts are reported, the ablation covers only three of seven tasks, and the method description lacks the detail needed for verification. The significance of the contribution is therefore conditional on a substantially strengthened empirical evaluation.

major comments (4)

[Section 4.2, Table 1] The central quantitative claim rests on an uncontrolled comparison. Table 1 states 'The results of all the competitors are from [29]', and the CogVideoX-5B row in Table 2 uses the same 61.64% value, so the authors did not re-run their own baseline with their own prompt template, resolution, seed set, and evaluation code. Without a matched-protocol re-run and without reporting variance or the number of seeds, the 25.32-percentage-point Consist-attr improvement and the ablation increments in Table 2 are not evidenced; the headline gain could be an artifact of protocol mismatch. Please re-run the baseline and at least the strongest open-source competitor under the authors' pipeline, report mean and standard deviation over multiple seeds, and make the evaluation code public.
[Sections 3.2 and 3.3] The method description is not detailed enough to be verified or reproduced. The attention-mask construction lacks a formal definition: the paper does not specify how text tokens are assigned to entity classes, how the T2V/V2T mask is computed from dynamic bounding boxes in latent coordinates, how overlapping boxes are merged at each timestep, or how the global prompt and entity-level prompt are combined in the text encoder. Similarly, the entity-consistency constraint is described only verbally: the reader is not told at which denoising steps the first-frame reference features are extracted, how they are injected (addition, replacement, or blending), or what fusion weight schedule is used. The LLM prompt template and decoding settings are also omitted. These details are central to a training-free wrapper and should be given as pseudo-code or an algorithm box.
[Table 2, Section 4.3] The ablation is incomplete and does not support the broad claim. Only Consist-attr, Spatial, and Numeracy are ablated; Dynamic-attr, Motion, Action, and Interaction are not, yet the abstract claims 'significantly improving performance.' On Dynamic-attr the full method gains only 0.02 percentage points over the baseline (2.21 vs 2.19), which, without variance estimates, is indistinguishable from noise. The sentence in Section 4.2 that DyST-XL 'also exhibits the base CogVideoX model' for Dynamic-attr is also incomplete. Please ablate all seven tasks and provide confidence intervals or per-seed results, and temper the significance claims accordingly.
[Section 3.1, Step 2] The load-bearing assumption that LLM-generated bounding boxes are accurate enough to serve as hard attention masks is unvalidated. If DeepSeek-R1 misplaces a box, misses an entity, or produces an implausible interpolated trajectory, the mask either removes the entity's text influence or binds it to the wrong region, and there is no fallback. The paper reports no parse accuracy, no layout accuracy (e.g., against detection models), and no failure-case analysis. Please add a quantitative validation of the planner (e.g., box IoU with detector outputs or human ratings), report how often planning failures occur on T2V-CompBench prompts, and discuss the effect of such failures on the final metrics.

minor comments (5)

[Section 4.2] The text says the baseline CogVideoX-5B 'scored 25.32%' for Consist-attr, but Table 1 and Table 2 both report 61.64%; this internal inconsistency should be corrected.
[Related Work, reference [41]] The in-text citation 'Regional Prompting FLUX [41]' does not match the reference listed as 'Training-Free Motion-Guided Video Generation with Enhanced Temporal Consistency Using Motion Consistency Loss'; the citation or the reference entry appears to be wrong and should be fixed.
[Abstract and Section 3.1] Linear interpolation of bounding boxes is described as 'physics-aware' and as using 'velocity profiles,' but no physical model is introduced; please temper the wording or justify the terminology.
[Section 4.2] The phrase 'DyST-XL also achieves a higher accuracy of 2.63% compared to the baseline model' should read '2.63 percentage points higher' to avoid ambiguity.
[Figures 5 and 6] The prompt in Figure 5 is written as 'Spherical globe rotating next to a cube clock' while Figure 6 uses 'Spherical globe cube clock'; the prompts should be consistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the method's components are defined independently of the evaluation metrics, and all benchmark comparisons derive from the external T2V-CompBench protocol.

full rationale

We walked the claimed derivation chain. The Dynamic Layout Planner uses DeepSeek-R1 to parse prompts and generate entity boxes; the Dual-Prompt Controlled Attention masks CogVideoX's cross-attention using those boxes; the Entity-Consistency Constraint copies first-frame features into later frames during denoising. None of these mechanisms is defined in terms of the T2V-CompBench scores or the specific evaluators (Grid-LLaVA, D-LLaVA, Grounding-DINO, DOT), and no target metric is used to fit any parameter that is then called a prediction. The paper explicitly states that 'The results of all the competitors are from [29]', so the baseline and competitor numbers are imported from an independent benchmark rather than derived from the paper's own equations; this raises matched-protocol and reproducibility concerns (no re-runs, no seeds, no error bars), but that is a correctness/evidential issue, not circularity. The ablation study tunes modules on the same benchmark and inherits its prompt protocol, but the increments are not forced by construction because the baseline was not re-run under the same rewritten-prompt condition. The only textual anomaly is the prose claim that CogVideoX-5B 'scored 25.32%' while Table 1 reports 61.64%; this is an internal inconsistency, not a circular reduction. No self-citation is load-bearing, and no uniqueness theorem or ansatz is imported from the authors' prior work. Therefore no circular step can be exhibited with a specific quote and reduction, and the appropriate score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The method rests on four unverified assumptions: the LLM plans correct layouts, linear interpolation approximates physical motion, masked attention preserves CogVideoX behavior, and first-frame feature injection maintains identity. No free parameters are reported; the feature-injection schedule and LLM decoding settings are hidden degrees of freedom. The paper introduces no new physical entities.

free parameters (2)

Entity-consistency feature injection strength and schedule = unreported
Section 3.3 says first-frame reference features are 'injected' into later frames, but no equation, coefficient, or denoising-step schedule is given; if tuned on T2V-CompBench, it is a hidden free parameter.
LLM planning decoding settings and prompt template = unreported
Layouts depend on DeepSeek-R1 outputs; temperature and the exact chain-of-thought prompt are not printed in the paper, so planning outputs are not reproducible.

assumptions (4)

domain assumption LLM produces correct entity, attribute, and bounding-box trajectory decompositions for arbitrary compositional prompts.
Section 3.1 Steps 1-2 relies on DeepSeek-R1 chain-of-thought output without validation or error handling; wrong boxes directly misplace masked attention.
ad hoc to paper Linear interpolation between initial and final bounding boxes yields physically plausible motion.
Section 3.1 Step 2 defines intermediate regions by temporal linear interpolation; this ignores acceleration, collisions, and occlusion, so 'physics-aware' is an assumption rather than a model.
domain assumption Replacing CogVideoX's text-video attention masks with entity-region masks preserves calibrated denoising.
Section 3.2 modifies cross-attention without retraining or theoretical justification; if the model relies on global context, masking could degrade generation.
domain assumption First-frame reference features can be injected into later-frame regions without creating artifacts or identity bleed.
Section 3.3 assumes feature propagation strengthens consistency; no analysis of when propagation fails (for example under large pose or scale changes) is given.

how reviews work

0 comments

Cite this review

Pith. "Pith review of DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation." pith.science (2026). https://pith.science/paper/FPOWRNIE

@misc{pith2026250415032,
  author       = {Pith},
  title        = {Pith review of: DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FPOWRNIE}},
  note         = {Machine review of arXiv:2504.15032}
}

read the original abstract

Compositional text-to-video generation, which requires synthesizing dynamic scenes with multiple interacting entities and precise spatial-temporal relationships, remains a critical challenge for diffusion-based models. Existing methods struggle with layout discontinuity, entity identity drift, and implausible interaction dynamics due to unconstrained cross-attention mechanisms and inadequate physics-aware reasoning. To address these limitations, we propose DyST-XL, a \textbf{training-free} framework that enhances off-the-shelf text-to-video models (e.g., CogVideoX-5B) through frame-aware control. DyST-XL integrates three key innovations: (1) A Dynamic Layout Planner that leverages large language models (LLMs) to parse input prompts into entity-attribute graphs and generates physics-aware keyframe layouts, with intermediate frames interpolated via trajectory optimization; (2) A Dual-Prompt Controlled Attention Mechanism that enforces localized text-video alignment through frame-aware attention masking, achieving precise control over individual entities; and (3) An Entity-Consistency Constraint strategy that propagates first-frame feature embeddings to subsequent frames during denoising, preserving object identity without manual annotation. Experiments demonstrate that DyST-XL excels in compositional text-to-video generation, significantly improving performance on complex prompts and bridging a crucial gap in training-free video synthesis. The code is released in https://github.com/XiaoBuL/DyST-XL.

Figures

Figures reproduced from arXiv: 2504.15032 by the authors.

**Figure 2.** Illustration of our compositional text-to-video generation process. An LLM is used to parse the input prompt to [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Qualitative comparison on complex prompts. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Qualitative visualization comparison with current text-to-video and compositional generation models. The prompts [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Qualitative ablation studies on different modules. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Attention heatmap visualization for CogVideoX-5B [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

45 extracted references · 19 canonical work pages

[29]

Kaiyue Sun, Kaiyi Huang, Xian Liu, Yue Wu, Zihan Xu, Zhenguo Li, and Xihui Liu. 2024. T2v-compbench: A comprehensive benchmark for compositional text-to-video generation. arXiv preprint arXiv:2407.14505 (2024)

arXiv 2024
[41]

Xinyu Zhang, Zicheng Duan, Dong Gong, and Lingqiao Liu. 2025. Training-Free Motion-Guided Video Generation with Enhanced Temporal Consistency Using Motion Consistency Loss. arXiv preprint arXiv:2501.07563 (2025)

arXiv 2025
[1]

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Floren- cia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

arXiv 2023
[2]

Capcut. 2024. Dreamina. https://dreamina.capcut.com/ai-tool/home

work page 2024
[3]

Haoxin Chen, Yong Zhang, Xiaodong Cun, Menghan Xia, Xintao Wang, Chao Weng, and Ying Shan. 2024. Videocrafter2: Overcoming data limitations for high-quality video diffusion models. In CVPR. 7310–7320

work page 2024
[4]

Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. 2024. Scaling rectified flow transformers for high-resolution image synthesis. InICML

2024
[5]

Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al . 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948 (2025)

arXiv 2025
[6]

Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. 2023. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307.04725 (2023)

arXiv 2023

Show all 45 references

[7]

Yingqing He, Tianyu Yang, Yong Zhang, Ying Shan, and Qifeng Chen. 2022. Latent video diffusion models for high-fidelity long video generation. arXiv preprint arXiv:2211.13221 (2022)

2022 arXiv
[8]

Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. NeurIPS 33, 6840–6851

2020
[9]

Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Hen- schel, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. 2023. Text2video-zero: Text-to-image diffusion models are zero-shot video genera- tors. In ICCV. 15954–15964

2023
[10]

Kuaishou. 2024. Kling. https://kling.kuaishou.com

2024
[11]

Black Forest Labs. 2024. FLUX. https://github.com/black-forest-labs/flux

2024
[12]

Guillaume Le Moing, Jean Ponce, and Cordelia Schmid. 2024. Dense optical tracking: Connecting the dots. In CVPR. 19187–19197

2024
[13]

Daeun Lee, Jaehong Yoon, Jaemin Cho, and Mohit Bansal. 2024. VideoRepair: Improving Text-to-Video Generation via Misalignment Evaluation and Localized Refinement. arXiv preprint arXiv:2411.15115 (2024)

2024 arXiv
[14]

Jiachen Li, Qian Long, Jian Zheng, Xiaofeng Gao, Robinson Piramuthu, Wenhu Chen, and William Yang Wang. 2024. T2v-turbo-v2: Enhancing video generation model post-training through data, reward, and conditional guidance design.arXiv preprint arXiv:2410.05677 (2024)

2024
[15]

Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. 2023. Gligen: Open-set grounded text-to-image generation. (2023), 22511–22521

2023
[16]

Long Lian, Baifeng Shi, Adam Yala, Trevor Darrell, and Boyi Li. 2023. Llm- grounded video diffusion models. arXiv preprint arXiv:2309.17444 (2023)

2023 arXiv
[17]

Bin Lin, Yunyang Ge, Xinhua Cheng, Zongjian Li, Bin Zhu, Shaodong Wang, Xianyi He, Yang Ye, Shenghai Yuan, Liuhan Chen, et al. 2024. Open-sora plan: Open-source large video generation model. arXiv preprint arXiv:2412.00131 (2024)

2024 arXiv
[18]

Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al . 2023. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499 (2023)

2023 arXiv
[19]

Guoqing Ma, Haoyang Huang, Kun Yan, Liangyu Chen, Nan Duan, Shengming Yin, Changyi Wan, Ranchen Ming, Xiaoniu Song, Xing Chen, et al. 2025. Step- video-t2v technical report: The practice, challenges, and future of video founda- tion model. arXiv preprint arXiv:2502.10248 (2025)

2025 arXiv
[20]

Xin Ma, Yaohui Wang, Gengyun Jia, Xinyuan Chen, Ziwei Liu, Yuan-Fang Li, Cunjian Chen, and Yu Qiao. 2024. Latte: Latent diffusion transformer for video generation. arXiv preprint arXiv:2401.03048 (2024)

2024 arXiv
[21]

William Peebles and Saining Xie. 2023. Scalable Diffusion Models with Trans- formers. In ICCV. 4172–4182

2023
[22]

Pika. 2024. Pika. https://www.pika.art

2024
[23]

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-Resolution Image Synthesis with Latent Diffusion Models. In CVPR. 10674–10685

2022
[24]

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In MICCAI. 234–241

2015
[25]

Runway. 2024. Gen-2: Generate novel videos with text, images or video clips. https://research.runwayml.com/gen2

2024
[26]

Runway. 2024. Introducing gen-3 alpha: A new frontier for video generation. https://runwayml.com/blog/introducing-gen-3-alpha

2024
[27]

Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020. Denoising Diffusion Implicit Models. CoRR abs/2010.02502 (2020). arXiv:2010.02502 https://arxiv.org/ abs/2010.02502

2020 arXiv
[28]

Spencer Sterling. 2023. Zeroscope. https://huggingface.co/cerspense/zeroscope_ v2_576w

2023
[30]

Genmo Team. 2024. Mochi 1. https://github.com/genmoai/models

2024
[31]

Ye Tian, Ling Yang, Haotian Yang, Yuan Gao, Yufan Deng, Xintao Wang, Zhaochen Yu, Xin Tao, Pengfei Wan, Di ZHANG, et al. 2024. Videotetris: Towards composi- tional text-to-video generation. NeurIPS 37 (2024), 29489–29513

2024
[32]

Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, and Shiwei Zhang. 2023. Modelscope text-to-video technical report. arXiv preprint arXiv:2308.06571 (2023)

2023 arXiv
[33]

Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, and Ishan Misra. 2024. Instancediffusion: Instance-level control for image generation. In CVPR. 6232–6242

2024
[34]

Zun Wang, Jialu Li, Han Lin, Jaehong Yoon, and Mohit Bansal. 2024. Dream- Runner: Fine-Grained Storytelling Video Generation with Retrieval-Augmented Motion Adaptation. arXiv preprint arXiv:2411.16657 (2024)

2024
[35]

Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. 2023. Tune-a- video: One-shot tuning of image diffusion models for text-to-video generation. In ICCV. 7623–7633

2023
[36]

Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, and Bin Cui
[37]

Xingyi Yang and Xinchao Wang. 2024. Compositional video generation as flow equalization. arXiv preprint arXiv:2407.06182 (2024)

2024 arXiv
[38]

Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al . 2024. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072 (2024)

2024 arXiv
[39]

David Junhao Zhang, Jay Zhangjie Wu, Jia-Wei Liu, Rui Zhao, Lingmin Ran, Yuchao Gu, Difei Gao, and Mike Zheng Shou. 2024. Show-1: Marrying pixel and latent diffusion models for text-to-video generation. IJCV (2024), 1–15

2024
[40]

Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023. Adding Conditional Control to Text-to-Image Diffusion Models. In ICCV. 3813–3824

2023
[42]

Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. 2024. Open-sora: Democratizing efficient video production for all. arXiv preprint arXiv:2412.20404 (2024)

2024 arXiv
[43]

Dewei Zhou, You Li, Fan Ma, Xiaoting Zhang, and Yi Yang. 2024. Migc: Multi- instance generation controller for text-to-image synthesis. In CVPR. 6818–6828

2024
[44]

Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023. Minigpt-4: Enhancing vision-language understanding with advanced large lan- guage models. arXiv preprint arXiv:2304.10592 (2023)

2023 arXiv
[2024]

arXiv preprint arXiv:2401.11708 (2024)

Mastering text-to-image diffusion: Recaptioning, planning, and generating with multimodal llms. arXiv preprint arXiv:2401.11708 (2024)

2024 arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [29]

Kaiyue Sun, Kaiyi Huang, Xian Liu, Yue Wu, Zihan Xu, Zhenguo Li, and Xihui Liu. 2024. T2v-compbench: A comprehensive benchmark for compositional text-to-video generation. arXiv preprint arXiv:2407.14505 (2024)

arXiv 2024

[2] [41]

Xinyu Zhang, Zicheng Duan, Dong Gong, and Lingqiao Liu. 2025. Training-Free Motion-Guided Video Generation with Enhanced Temporal Consistency Using Motion Consistency Loss. arXiv preprint arXiv:2501.07563 (2025)

arXiv 2025

[3] [1]

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Floren- cia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

arXiv 2023

[4] [2]

Capcut. 2024. Dreamina. https://dreamina.capcut.com/ai-tool/home

work page 2024

[5] [3]

Haoxin Chen, Yong Zhang, Xiaodong Cun, Menghan Xia, Xintao Wang, Chao Weng, and Ying Shan. 2024. Videocrafter2: Overcoming data limitations for high-quality video diffusion models. In CVPR. 7310–7320

work page 2024

[6] [4]

Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. 2024. Scaling rectified flow transformers for high-resolution image synthesis. InICML

2024

[7] [5]

Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al . 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948 (2025)

arXiv 2025

[8] [6]

Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. 2023. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307.04725 (2023)

arXiv 2023