Pith. sign in

REVIEW 4 major objections 4 minor 14 cited by

Captain Cinema: Towards Short Movie Generation

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read Captain Cinema claims to turn a text storyline into a coherent multi-scene short movie by separating keyframe planning from video synthesis, with compressed visual memory keeping characters and scenes stable across dozens of shots.

desk verdict Genuinely clever context compression and a sensible two-stage design, but the quantitative evaluation overclaims and the load-bearing keyframe mismatch is never tested. read the letter →

arxiv 2507.18634 v1 pith:7K22JRNH submitted 2025-07-24 cs.CV

Junfei Xiao , Ceyuan Yang , Lvmin Zhang , Shengqu Cai , Yang Zhao , Yuwei Guo , Gordon Wetzstein , Maneesh Agrawala
show 2 more authors
Alan Yuille Lu Jiang
This is my paper
classification cs.CV
keywords shortmoviegenerationkeyframeplanningvideodiffusionmultimodaltransformerlong-contextvisualmemorycompressionnarrativecoherencecharacterconsistency
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 proposes Captain Cinema, a generation framework that turns a detailed text storyline into a multi-scene short movie. It separates the task into top-down keyframe planning, where a text-to-image model generates a storyboard of sparse keyframes, and bottom-up video synthesis, where a video model fills in the motion between those keyframes. The central claim is that this separation, together with compressed visual memory, lets the system keep characters, scenes, and narrative consistent across dozens of shots, far beyond the five-to-ten-second clips of base video generators. A sympathetic reading is that this is a concrete step toward fully automated, story-driven movie generation.

What carries the argument

GoldenMem is the central memory mechanism: it stores the newest keyframe at full resolution and encodes earlier frames at progressively coarser scales using the golden ratio, producing an inverse Fibonacci sequence of token counts whose total stays within a constant factor above the cost of the most recent frame. Alongside it, hybrid attention masking in the multimodal diffusion transformer keeps early transformer blocks local and later blocks global, enabling coherent interleaved keyframe generation, while the bottom-up video model is fine-tuned with multi-keyframe interleaved conditioning. Progressive long-context fine-tuning and dynamic stride sampling make training stable, and semantic-oriented retrieval, using CLIP text-to-image embeddings over T5 text-to-text, supports non-linear narrative structures such as flashbacks.

What would settle it

Generate a long movie with one deliberately swapped or corrupted keyframe at an interior position, then measure identity consistency before and after that frame; if the bottom-up video model propagates the error without correction and the consistency score drops, the assumption that separate keyframe planning suffices is exposed. Alternatively, train an end-to-end image-to-video variant on the same data and compare identity retention under the same corruption, since the paper's claim implies the separate pipeline is enough.

Watch

Extended reading notes

Core claim

On the paper's own terms, Captain Cinema achieves state-of-the-art results in long narrative movie generation by disentangling global narrative planning from local motion synthesis. A top-down model generates a sequence of keyframes that outline the entire storyline, and a bottom-up video model, conditioned on all the keyframes, synthesizes the spatio-temporal dynamics between them. The GoldenMem mechanism compresses earlier visual context through golden-ratio downsampling, which yields an inverse Fibonacci sequence of latent sizes and keeps the conditioning token cost at roughly 1.62 times the single-frame cost. In a long-context stress test growing from 8 to 48 interleaved pairs, the paper reports that the method retains more than 93 percent of its 8-pair consistency score, while the LCT baseline degrades sharply. The paper also reports better temporal dynamics, visual quality, and user-study ratings than the IC-LoRA-plus-image-to-video baseline.

Load-bearing premise

The whole pipeline assumes that the sparse keyframes produced by the top-down model are trustworthy anchors: if a keyframe drifts in identity, clothing, or scene, the bottom-up video model inherits that error and has no way to fix it, because the two stages are trained separately.

Editorial extensions

If this is right

  • If the central claim is right, a single text storyline can be expanded into a coherent multi-scene short film, replacing shot-by-shot manual production.
  • The GoldenMem compression bound means history length can grow, from 32 to 48 pairs in the paper, without running out of memory, so longer films become feasible on fixed hardware.
  • The framework supports cross-movie character swapping and creative scene recombination, suggesting that character identity and scene context are learned as separable factors.
  • Because the video generator conditions on multiple keyframes rather than a single first frame, motion dynamics stay stable across shot boundaries, not only within one shot.
  • The reported stress-test numbers imply the method is robust to context-length growth, whereas the LCT baseline degrades sharply as the context window expands.

Reading between the lines

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

  • If keyframe anchors are reliable, the same top-down and bottom-up split could be applied to other long-form modalities, such as multi-scene audio or interactive game cutscenes, where sparse planning anchors dense generation.
  • A testable extension is to corrupt or swap a single keyframe mid-sequence and measure identity consistency; the paper's stated limitation predicts the video model will inherit the error rather than correct it.
  • The golden-ratio compression suggests a concrete efficiency benchmark: an end-to-end image-to-video model trained jointly could show whether separate training is a real bottleneck or merely a practical convenience.
  • Semantic-oriented retrieval over temporal order means non-linear storytelling is a design goal; one could quantify how much retrieval depth improves narrative coherence in flashback or foreshadowing structures.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper presents Captain Cinema, a two-stage framework for short movie generation from a textual storyline. A top-down keyframe planner (fine-tuned Flux with hybrid attention masking and GoldenMem memory compression) generates interleaved keyframes; a bottom-up video synthesizer (fine-tuned Seaweed with multi-keyframe conditioning) generates shots between keyframes. The authors also introduce progressive long-context fine-tuning, dynamic stride sampling, and a cinematic dataset of about 500 hours. Experiments compare against LCT and IC-LoRA+I2V using VBench-2.0 metrics and a user study, and include a long-context stress test. The central claim is state-of-the-art visual fidelity, temporal coherence, and robustness for long narrative movie generation.

Significance. If substantiated, the framework would be a useful step toward practical long-form video generation, and the GoldenMem token-budget derivation is a clean, self-contained contribution with a provable constant-factor overhead. The paper is also honest about its limitations. However, the evaluation is not currently strong enough to support the headline claims: the reported evidence lacks statistical grounding, key training hyperparameters are inconsistent, and the central train/inference mismatch is unmeasured. With additional experiments and corrected reporting, the work could be a solid system paper.

major comments (4)
  1. [Section 4 / Appendix A / Table 4] The training schedules for both modules are irreconcilable across three parts of the manuscript. Section 4 states the keyframe model is trained for 40,000 steps with batch size 32 on 32 H100 GPUs and the video model for 15,000 steps on 256 H100 GPUs; Appendix A states the keyframe model is trained for 2,500 steps and the video model for 20,000 steps; Table 4 states the keyframe model is trained for 20,000 steps per stage with batch size 16 and the video model for 10,000 total steps with batch size 64. These discrepancies are load-bearing because the paper's claims about progressive fine-tuning stability (Fig. 8) and the method's efficiency depend on the actual schedule, and no reader can determine the real configuration.
  2. [Section 3.3 / Section 5, limitation (1)] The bottom-up video generator is trained on real keyframe-conditioned shots, while at inference it conditions on keyframes generated by the top-down model. Section 5 explicitly lists the absence of end-to-end training as limitation (1), but no experiment in Section 4 measures the resulting quality drop: Table 1 compares whole pipelines, and Table 2 evaluates keyframe sequences and generated clips without isolating the train/inference mismatch. Because the central claim concerns end-to-end movie quality, the authors should add an ablation that conditions the video model on generated keyframes versus ground-truth keyframes, or otherwise quantify the error propagation.
  3. [Section 4.1, Table 2] The paper claims that 'our model retains > 93% of its 8-pair consistency even at 48 pairs.' This is not supported by Table 2: comparing the 48-pair GoldenMem row with the 8-pair Ours row gives scene consistency 3.0/3.9 (77%), narrative coherence 3.3/4.0 (83%), and identity 0.31/0.51 (61%). Even at 32 pairs without GoldenMem, character consistency is 4.5/4.9 (92%). The claim should be corrected to state which metric and which configuration support the 93% figure, or the robustness claim should be revised.
  4. [Section 4.1, Table 1] Table 1 reports VBench metrics and user-study scores without error bars, confidence intervals, or significance tests, and the user study does not report the number of participants, the number of videos rated, or inter-rater agreement. Given that the margins between Ours and LCT are small on several automatic metrics (Quality 61.7 vs 59.9, Consistency 91.0 vs 94.8*), the claim of performing 'favorably' is not statistically supported. At minimum, the paper should include standard deviations across clips and participant counts, and ideally a significance test.
minor comments (4)
  1. [Appendix A] Appendix A repeats the same implementation paragraph verbatim twice; please remove the duplicate.
  2. [References] Reference [60] is listed as 'Team Seawead'; this should be 'Team Seaweed'.
  3. [Figure 2] Figure 2's caption says 'to preserve IPs,' which is ambiguous; the intended word is likely 'identities.'
  4. [Table 4] Table 4's resolution entries ('400×800×L' for keyframes and '(480P,native AR)×T' for video) are not self-explanatory; please specify the meaning of L and T and the temporal duration of the video clips.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the pipeline's components are modular and the SOTA claim is benchmarked externally; self-citations to LCT, Seaweed, and VideoAuteur are transparent bases, not load-bearing.

full rationale

Walking the derivation chain, no load-bearing step reduces to its own input. The top-down keyframe planner and bottom-up video synthesizer are trained separately on interleaved data; the video model is conditioned on keyframes at inference, but the paper does not define keyframe quality in terms of final video quality, nor fit any parameter to the headline metrics. GoldenMem's total-token bound T = t0(1 + phi^-2 + phi^-4 + ...) < 1.62t0 is a mathematical consequence of the explicitly chosen inverse-Fibonacci downsampling schedule; it is a design property, not a prediction fitted to results. The LCT and Seaweed citations are self-citations (Refs. 38, 39, 60), and LCT is used as the primary baseline, but the paper additionally reports VBench-2.0 metrics, a user study, and long-context stress tests against LCT and IC-LoRA+I2V, so the central SOTA claim is not justified solely by citing prior work of overlapping authorship. Section 5, limitation (1), explicitly admits the frame-level and video-level modules are trained separately, so model-generated keyframes are never seen in video-model training; this is a genuine train/inference mismatch and a correctness risk, but it is not a circular reduction because the video output is not equivalent to the keyframe input by construction. The internal numerical inconsistency in the '>93%' retention claim (Table 2 gives 4.5/4.9 about 91.8%) and the narrow baseline set are concerns, but they are not circularity. Overall score 2 reflects only the minor, non-load-bearing presence of self-citations.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The system consumes external pretrained generators (Flux 1.Dev, Seaweed-3B, LCT), a dataset of copyrighted films annotated by Gemini, and several hand-chosen design constants (golden-ratio downsampling, warm-up step, noise range, stride overlap). The token-budget derivation is straightforward algebra. No new physical entities are introduced.

free parameters (5)
  • GoldenMem downsampling factor phi = (1+sqrt(5))/2 ~ 1.618
    Chosen by hand to motivate inverse-Fibonacci token decay; the claimed 1.62x overhead is derived from this choice and is not a general result.
  • Warm-up step for progressive fine-tuning = 40,000 steps
    Selected as the 'moderate' warm-up point after observing collapse with no warmup and artifacts with 80K-step warmup (Fig. 8).
  • Noise injection range for conditioning frames = 1 to 250 timesteps
    The ablation in Fig. 10 picks this range as best for character consistency; the design uses noise injection but the exact level is chosen from the ablation.
  • Dynamic stride overlap threshold = 25% overlap
    Threshold for counting a dynamically strided sampled sequence as valid; underpins the '100x more data' claim in Section 3.2.
  • Context-pair counts = 8, 16, 24, 32, 48
    Training and evaluation context lengths are design choices; the long-context robustness claim is measured at these specific points rather than at arbitrary lengths.
assumptions (6)
  • standard math Geometric series sum: sum_{i=0}^{inf} phi^{-2i} = phi^2/(phi^2-1).
    Used in the GoldenMem token-count derivation in Section 3.2; true by algebra.
  • domain assumption Pretrained Flux 1.Dev and Seaweed-3B retain generative quality after progressive long-context fine-tuning.
    The method depends on successful warm-up; the paper admits direct fine-tuning collapses and excessive warmup produces artifacts (Section 4.2, Fig. 8).
  • domain assumption Scene-cut keyframes extracted by PySceneDetect are sufficient anchors for the bottom-up video model.
    Keyframe extraction and the two-stage design rely on this; the authors state that the frame and video modules are trained separately and the gap is a limitation (Section 5).
  • domain assumption Gemini 2.x is a reliable judge for consistency, diversity, and narrative coherence.
    Gemini Flash 2.5 scores the long-context stress test (Table 2, Appendix D) with no validation against human labels reported.
  • domain assumption Interleaved conditioning plus noise-injected context preserves identity without explicit identity constraints.
    The out-of-domain identity tests in Appendix E rely on 12 context images and captions; no identity-specific loss or embedding is described.
  • domain assumption PySceneDetect scene boundaries correspond to meaningful narrative shot boundaries.
    The data pipeline (Section 3.1) uses scene cuts to define shots and keyframes; if the detector mis-segments, the interleaved pairs lose narrative structure.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Captain Cinema: Towards Short Movie Generation." pith.science (2026). https://pith.science/paper/7K22JRNH

@misc{pith2026250718634,
  author       = {Pith},
  title        = {Pith review of: Captain Cinema: Towards Short Movie Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7K22JRNH}},
  note         = {Machine review of arXiv:2507.18634}
}
read the original abstract

We present Captain Cinema, a generation framework for short movie generation. Given a detailed textual description of a movie storyline, our approach firstly generates a sequence of keyframes that outline the entire narrative, which ensures long-range coherence in both the storyline and visual appearance (e.g., scenes and characters). We refer to this step as top-down keyframe planning. These keyframes then serve as conditioning signals for a video synthesis model, which supports long context learning, to produce the spatio-temporal dynamics between them. This step is referred to as bottom-up video synthesis. To support stable and efficient generation of multi-scene long narrative cinematic works, we introduce an interleaved training strategy for Multimodal Diffusion Transformers (MM-DiT), specifically adapted for long-context video data. Our model is trained on a specially curated cinematic dataset consisting of interleaved data pairs. Our experiments demonstrate that Captain Cinema performs favorably in the automated creation of visually coherent and narrative consistent short movies in high quality and efficiency. Project page: https://thecinema.ai

Discussion (0). Sign in to comment.

Forward citations

Cited by 14 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GroundShot: Visually Consistent Multi-Shot Long Video Generation via Entity-Grounded Shot Scheduling

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    GroundShot introduces entity-grounded shot scheduling with online visual memory to improve consistency in multi-shot video generation and presents GroundBench for entity-level evaluation.

  2. DeltaCam: Differential Intrinsic Camera Modeling for Video Generation

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    DeltaCam models relative changes in camera intrinsics via Δ-parameterized neural adaptors in video diffusion models trained on synthetic data to enable controllable generation and real-world transfer.

  3. EM-Vid: Training-Free Entity-Centric Memory for Efficient and Consistent Multi-Shot Video Generation

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    EM-Vid introduces an entity-centric latent patch memory bank with sparse token conditioning and budgeted updates for training-free consistent multi-shot video generation.

  4. Camera Artist: A Multi-Agent Framework for Cinematic Language Storytelling Video Generation

    cs.AI 2026-04 unverdicted novelty 7.0 of 10

    Camera Artist is a multi-agent framework introducing a Cinematography Shot Agent with recursive storyboard generation and cinematic language injection to improve narrative consistency and film quality in AI-generated ...

  5. DramaDirector: Geometry-Guided Short Drama Generation

    cs.CV 2026-06 conditional novelty 6.0 of 10

    Geometry-indexed depth–pose retrieval plus schema SFT and GRPO planning improves faithfulness, consistency, and controllability of plot-to-short-drama video generation over multi-agent and text-only baselines.

  6. UnityShots: Memory-Driven Multi-Shot Audio-Video Generation with Boundary-Aware Gating

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    UnityShots uses fixed LTM and STM memory slots with boundary-conditioned gating and speaker tokens to achieve coherent multi-shot audio-video generation, leading open-source baselines on cross-shot coherence metrics.

  7. GroundShot: Visually Consistent Multi-Shot Long Video Generation via Entity-Grounded Shot Scheduling

    cs.CV 2026-06 conditional novelty 6.0 of 10

    A training-free framework that reorders shot generation and maintains per-entity visual memory improves cross-shot character, object, and scene consistency over narrative-order memory baselines.

  8. Enhancing Train-Free Infinite-Frame Generation for Consistent Long Videos

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    MIGA introduces two-stage alignment to close train-inference gaps and dual consistency enhancement via self-reflection and long-range guidance to achieve SOTA temporal consistency in infinite-frame video generation on...

  9. DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    DreamShot uses video diffusion priors and a role-attention consistency loss to produce coherent, personalized storyboards with better character and scene continuity than text-to-image methods.

  10. PhyDetEx: Detecting and Explaining the Physical Plausibility of T2V Models

    cs.CV 2025-12 conditional novelty 6.0 of 10

    A new dataset and fine-tuned VLM detector/explainer called PhyDetEx shows that current T2V models still struggle to generate videos that obey physical laws, with open-source models performing worse.

  11. DramaDirector: Geometry-Guided Short Drama Generation

    cs.CV 2026-06 unverdicted novelty 5.0 of 10

    DramaDirector retrieves depth-pose references from real drama shots to guide first-frame and image-to-video synthesis for plot-driven short dramas, paired with the DramaBoard benchmark.

  12. DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    DreamShot generates controllable multi-shot storyboards from text or character references by exploiting video diffusion priors and a Role-Attention Consistency Loss.

  13. BIFE: Better Interaction, Fewer Errors for Minute-Long Video Generation

    cs.CV 2025-11 conditional novelty 5.0 of 10

    BlockVid generates minute-long videos with a semantic sparse KV cache, Block Forcing training, and chunk-level noise scheduling, reporting large gains on its own LV-Bench and on VBench.

  14. Towards Error-Free Long Video Generation

    cs.CV 2026-06 unverdicted novelty 4.0 of 10

    An autoregressive diffusion framework with causal inter-clip attention, KV caching, and truncation-rectified flow produces coherent minute-level videos while reducing error accumulation.

Reference graph

Works this paper leans on

63 extracted references · 41 canonical work pages · cited by 11 Pith papers

  1. [1]

    The narrative construction of reality.Critical inquiry, 18(1):1–21, 1991

    Jerome Bruner. The narrative construction of reality.Critical inquiry, 18(1):1–21, 1991

    work page 1991

  2. [2]

    Random House, 2014

    Yuval Noah Harari.Sapiens: A brief history of humankind. Random House, 2014

    work page 2014

  3. [3]

    Video diffusion models

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. InNeurlPS, 2022

    work page 2022

  4. [4]

    Make-a-video: Text-to-video generation without text-video data

    Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. InarXiv, 2022

    work page 2022

  5. [5]

    Videocrafter1: Open diffusion models for high-quality video generation

    Haoxin Chen, Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, et al. Videocrafter1: Open diffusion models for high-quality video generation. In arXiv, 2023

    work page 2023

  6. [6]

    Videocrafter2: Overcoming data limitations for high-quality video diffusion models

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

    work page 2024

  7. [7]

    Imagen video: High definition video generation with diffusion models

    Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion models. InarXiv, 2022

    work page 2022

  8. [8]

    Modelscope text-to-video technical report

    Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, and Shiwei Zhang. Modelscope text-to-video technical report. InarXiv, 2023

    work page 2023

Show all 63 references
  1. [9]

    Magicvideo: Efficient video generation with latent diffusion models

    Daquan Zhou, Weimin Wang, Hanshu Yan, Weiwei Lv, Yizhe Zhu, and Jiashi Feng. Magicvideo: Efficient video generation with latent diffusion models. InarXiv, 2022

    2022

  2. [10]

    Scalable diffusion models with transformers

    William Peebles and Saining Xie. Scalable diffusion models with transformers. InICCV, 2023

    2023

  3. [11]

    Video generation models as world simulators, 2024

    Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, et al. Video generation models as world simulators, 2024

    2024

  4. [12]

    Cogvideo: Large-scale pretraining for text-to-video generation via transformers

    Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang. Cogvideo: Large-scale pretraining for text-to-video generation via transformers. InarXiv, 2022

    2022

  5. [13]

    Cogvideox: Text-to-video diffusion models with an expert transformer

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

    2024

  6. [14]

    Videopoet: A large language model for zero-shot video generation

    Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, et al. Videopoet: A large language model for zero-shot video generation. In ICML, 2024

    2024

  7. [15]

    Emu: Generative pretraining in multimodality

    Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, and Xinlong Wang. Emu: Generative pretraining in multimodality. InICLR, 2023

    2023

  8. [16]

    Generative multimodal models are in-context learners

    Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiying Yu, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, and Xinlong Wang. Generative multimodal models are in-context learners. InCVPR, 2024

    2024

  9. [17]

    Emu3: Next-token prediction is all you need

    Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. InarXiv, 2024

    2024

  10. [18]

    Livephoto: Real image animation with text-guided motion control

    Xi Chen, Zhiheng Liu, Mengting Chen, Yutong Feng, Yu Liu, Yujun Shen, and Hengshuang Zhao. Livephoto: Real image animation with text-guided motion control. InECCV, 2025

    2025

  11. [19]

    Magicanimate: Temporally consistent human image animation using diffusion model

    Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew, Hanshu Yan, Jia-Wei Liu, Chenxu Zhang, Jiashi Feng, and Mike Zheng Shou. Magicanimate: Temporally consistent human image animation using diffusion model. InCVPR, 2024

    2024

  12. [20]

    Ccedit: Creative and controllable video editing via diffusion models

    Ruoyu Feng, Wenming Weng, Yanhui Wang, Yuhui Yuan, Jianmin Bao, Chong Luo, Zhibo Chen, and Baining Guo. Ccedit: Creative and controllable video editing via diffusion models. InCVPR, 2024

    2024

  13. [21]

    Pix2video: Video editing using image diffusion

    Duygu Ceylan, Chun-Hao P Huang, and Niloy J Mitra. Pix2video: Video editing using image diffusion. InCVPR, 2023. 12

    2023

  14. [22]

    Style-a-video: Agile diffusion for arbitrary text-based video style transfer

    Nisha Huang, Yuxin Zhang, and Weiming Dong. Style-a-video: Agile diffusion for arbitrary text-based video style transfer. IEEE Signal Processing Letters, 2024

    2024

  15. [23]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. InCVPR, 2022

    2022

  16. [24]

    Dreamllm: Synergistic multimodal comprehension and creation

    Runpei Dong, Chunrui Han, Yuang Peng, Zekun Qi, Zheng Ge, Jinrong Yang, Liang Zhao, Jianjian Sun, Hongyu Zhou, Haoran Wei, et al. Dreamllm: Synergistic multimodal comprehension and creation. InarXiv, 2023

    2023

  17. [25]

    Mm-interleaved: Interleaved image-text generative modeling via multi-modal feature synchronizer

    Changyao Tian, Xizhou Zhu, Yuwen Xiong, Weiyun Wang, Zhe Chen, Wenhai Wang, Yuntao Chen, Lewei Lu, Tong Lu, Jie Zhou, et al. Mm-interleaved: Interleaved image-text generative modeling via multi-modal feature synchronizer. In arXiv, 2024

    2024

  18. [26]

    Seed-x: Multimodal models with unified multi-granularity comprehension and generation

    Yuying Ge, Sijie Zhao, Jinguo Zhu, Yixiao Ge, Kun Yi, Lin Song, Chen Li, Xiaohan Ding, and Ying Shan. Seed-x: Multimodal models with unified multi-granularity comprehension and generation. InarXiv, 2024

    2024

  19. [27]

    Seed-story: Multimodal long story generation with large language model

    Shuai Yang, Yuying Ge, Yang Li, Yukang Chen, Yixiao Ge, Ying Shan, and Yingcong Chen. Seed-story: Multimodal long story generation with large language model. InarXiv, 2024

    2024

  20. [28]

    Flamingo: a visual language model for few-shot learning

    Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. In NeurlPS, 2022

    2022

  21. [29]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021

    2021

  22. [30]

    Scaling language-image pre-training via masking

    Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, and Kaiming He. Scaling language-image pre-training via masking. InCVPR, 2023

    2023

  23. [31]

    Eva-clip: Improved training techniques for clip at scale

    Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang, and Yue Cao. Eva-clip: Improved training techniques for clip at scale. InarXiv, 2023

    2023

  24. [32]

    Datacomp: In search of the next generation of multimodal datasets

    Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, et al. Datacomp: In search of the next generation of multimodal datasets. InNeurlPS, 2024

    2024

  25. [33]

    Laion-5b: An open large-scale dataset for training next generation image-text models

    Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. InNeurlPS, 2022

    2022

  26. [34]

    Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language...

    2023 arXiv

  27. [35]

    Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. InICML, 2023

    2023

  28. [36]

    Visual instruction tuning

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. InNeurlPS, 2024

    2024

  29. [37]

    Visionllm: Large language model is also an open-ended decoder for vision-centric tasks

    Wenhai Wang, Zhe Chen, Xiaokang Chen, Jiannan Wu, Xizhou Zhu, Gang Zeng, Ping Luo, Tong Lu, Jie Zhou, Yu Qiao, et al. Visionllm: Large language model is also an open-ended decoder for vision-centric tasks. In Advances in Neural Information Processing Systems, 2024

    2024

  30. [38]

    Videoauteur: Towards long narrative video generation.arXiv preprint arXiv:2501.06173, 2024

    Junfei Xiao, Feng Cheng, Lu Qi, Liangke Gui, Jiepeng Cen, Zhibei Ma, Alan Yuille, and Lu Jiang. Videoauteur: Towards long narrative video generation.arXiv preprint arXiv:2501.06173, 2024

    2024 arXiv

  31. [39]

    Long context tuning for video generation.arXiv preprint arXiv:2503.10589, 2025

    Yuwei Guo, Ceyuan Yang, Ziyan Yang, Zhibei Ma, Zhijie Lin, Zhenheng Yang, Dahua Lin, and Lu Jiang. Long context tuning for video generation.arXiv preprint arXiv:2503.10589, 2025

    2025 arXiv

  32. [40]

    Diffusion forcing: Next-token prediction meets full-sequence diffusion.Advancesin Neural Information Processing Systems, 37:24081–24125, 2024

    Boyuan Chen, Diego Martí Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzmann. Diffusion forcing: Next-token prediction meets full-sequence diffusion.Advancesin Neural Information Processing Systems, 37:24081–24125, 2024. 13

    2024

  33. [42]

    Pusa: Thousands timesteps video diffusion model, 2025

    Yaofang Liu and Rui Liu. Pusa: Thousands timesteps video diffusion model, 2025

    2025

  34. [43]

    Videodirectorgpt: Consistent multi-scene video generation via llm-guided planning

    Han Lin, Abhay Zala, Jaemin Cho, and Mohit Bansal. Videodirectorgpt: Consistent multi-scene video generation via llm-guided planning. InCOLM, 2024

    2024

  35. [44]

    Vlogger: Make your dream a vlog

    Shaobin Zhuang, Kunchang Li, Xinyuan Chen, Yaohui Wang, Ziwei Liu, Yu Qiao, and Yali Wang. Vlogger: Make your dream a vlog. InCVPR, 2024

    2024

  36. [45]

    Animate-a-story: Storytelling with retrieval-augmented video generation.arXiv preprint arXiv:2307.06940, 2023

    Yingqing He, Menghan Xia, Haoxin Chen, Xiaodong Cun, Yuan Gong, Jinbo Xing, Yong Zhang, Xintao Wang, Chao Weng, Ying Shan, et al. Animate-a-story: Storytelling with retrieval-augmented video generation.arXiv preprint arXiv:2307.06940, 2023

    2023 arXiv

  37. [46]

    Videotetris: Towards compositional text-to-video generation.arXiv preprint arXiv:2406.04277, 2024

    Ye Tian, Ling Yang, Haotian Yang, Yuan Gao, Yufan Deng, Jingmin Chen, Xintao Wang, Zhaochen Yu, Xin Tao, Pengfei Wan, et al. Videotetris: Towards compositional text-to-video generation.arXiv preprint arXiv:2406.04277, 2024

    2024 arXiv

  38. [47]

    In-context lora for diffusion transformers.arXiv preprint arXiv:2410.23775, 2024

    Lianghua Huang, Wei Wang, Zhi-Fan Wu, Yupeng Shi, Huanzhang Dou, Chen Liang, Yutong Feng, Yu Liu, and Jingren Zhou. In-context lora for diffusion transformers.arXiv preprint arXiv:2410.23775, 2024

    2024 arXiv

  39. [48]

    Storydiffusion: Consistent self-attention for long-range image and video generation.arXiv preprint arXiv:2405.01434, 2024

    Yupeng Zhou, Daquan Zhou, Ming-Ming Cheng, Jiashi Feng, and Qibin Hou. Storydiffusion: Consistent self-attention for long-range image and video generation.arXiv preprint arXiv:2405.01434, 2024

    2024 arXiv

  40. [49]

    Seed-story: Multimodal long story generation with large language model.arXiv preprint arXiv:2407.08683, 2024

    Shuai Yang, Yuying Ge, Yang Li, Yukang Chen, Yixiao Ge, Ying Shan, and Yingcong Chen. Seed-story: Multimodal long story generation with large language model.arXiv preprint arXiv:2407.08683, 2024

    2024 arXiv

  41. [50]

    Moviedreamer: Hierarchical generation for coherent long visual sequence.arXiv preprint arXiv:2407.16655, 2024

    Canyu Zhao, Mingyu Liu, Wen Wang, Jianlong Yuan, Hao Chen, Bo Zhang, and Chunhua Shen. Moviedreamer: Hierarchical generation for coherent long visual sequence.arXiv preprint arXiv:2407.16655, 2024

    2024

  42. [51]

    Packing input frame context in next-frame prediction models for video generation, 2025

    Lvmin Zhang and Maneesh Agrawala. Packing input frame context in next-frame prediction models for video generation, 2025

    2025

  43. [52]

    FlexTok: Resampling images into 1d token sequences of flexible length, 2025

    Roman Bachmann, Jesse Allardice, David Mizrahi, Enrico Fini, Oğuzhan Fatih Kar, Elmira Amirloo, Alaaeldin El-Nouby, Amir Zamir, and Afshin Dehghan. FlexTok: Resampling images into 1d token sequences of flexible length, 2025

    2025

  44. [53]

    Ltx-video: Realtime video latent diffusion.arXiv preprint arXiv:2501.00103, 2024

    Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, et al. Ltx-video: Realtime video latent diffusion.arXiv preprint arXiv:2501.00103, 2024

    2024 arXiv

  45. [54]

    Pyramidal flow matching for efficient video generative modeling, 2024

    Yang Jin, Zhicheng Sun, Ningyuan Li, Kun Xu, Kun Xu, Hao Jiang, Nan Zhuang, Quzhe Huang, Yang Song, Yadong Mu, and Zhouchen Lin. Pyramidal flow matching for efficient video generative modeling, 2024

    2024

  46. [55]

    Long-context autoregressive video modeling with next-frame prediction, 2025

    Yuchao Gu, Weijia Mao, and Mike Zheng Shou. Long-context autoregressive video modeling with next-frame prediction, 2025

    2025

  47. [56]

    Hitvideo: Hierarchical tokenizers for enhancing text-to-video generation with autoregressive large language models

    Ziqin Zhou, Yifan Yang, Yuqing Yang, Tianyu He, Houwen Peng, Kai Qiu, Qi Dai, Lili Qiu, Chong Luo, and Lingqiao Liu. Hitvideo: Hierarchical tokenizers for enhancing text-to-video generation with autoregressive large language models. arXiv preprint arXiv:2503.11513, 2025

    2025 arXiv

  48. [57]

    Pyscenedetect v0.6

    Breakthrough. Pyscenedetect v0.6. https://github.com/Breakthrough/PySceneDetect, 2021. Python-based video scene detection library

    2021

  49. [58]

    Gemini: A family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023

    Google Gemini Team. Gemini: A family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023

    2023 arXiv

  50. [59]

    Flux.https://github.com/black-forest-labs/flux, 2024

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

    2024

  51. [60]

    Seaweed-7b: Cost-effective training of video generation foundation model.arXiv preprint arXiv:2504.08685, 2025

    Team Seawead, Ceyuan Yang, Zhijie Lin, Yang Zhao, Shanchuan Lin, Zhibei Ma, Haoyuan Guo, Hao Chen, Lu Qi, Sen Wang, et al. Seaweed-7b: Cost-effective training of video generation foundation model.arXiv preprint arXiv:2504.08685, 2025. 14

    2025 arXiv

  52. [61]

    Vbench-2.0: Advancing video generation benchmark suite for intrinsic faithfulness.arXiv preprint arXiv:2503.21755, 2025

    Dian Zheng, Ziqi Huang, Hongbo Liu, Kai Zou, Yinan He, Fan Zhang, Yuanhan Zhang, Jingwen He, Wei-Shi Zheng, Yu Qiao, et al. Vbench-2.0: Advancing video generation benchmark suite for intrinsic faithfulness.arXiv preprint arXiv:2503.21755, 2025. 15 Appendix A Implementation Det...

    2025 arXiv

  53. [62]

    Character Consistency

  54. [63]

    Diversity (no duplicate or near-duplicate frames)

  55. [64]

    character_consistency

    Narrative Coherence (does the sequence form a sensible mini-story?) For each aspect, assign a 0–5 score and provide a one-sentence justification. Score meanings: | Score | Meaning | |-------|---------------------------------------------| | 0 | Unacceptable: completely fails ex...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.