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VideoTetris: Towards Compositional Text-to-Video Generation

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arxiv 2406.04277 v2 pith:GAFMGVBP submitted 2024-06-06 cs.CV

Ye Tian , Ling Yang , Haotian Yang , Yuan Gao , Yufan Deng , Jingmin Chen , Xintao Wang , Zhaochen Yu
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Xin Tao Pengfei Wan Di Zhang Bin Cui
This is my paper · ORCID
classification cs.CV
keywords generationcompositionalvideotetrisproposevideoattentioncomplexdata
verification ladder T0 review T1 audit T2 compute T3 formal
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Diffusion models have demonstrated great success in text-to-video (T2V) generation. However, existing methods may face challenges when handling complex (long) video generation scenarios that involve multiple objects or dynamic changes in object numbers. To address these limitations, we propose VideoTetris, a novel framework that enables compositional T2V generation. Specifically, we propose spatio-temporal compositional diffusion to precisely follow complex textual semantics by manipulating and composing the attention maps of denoising networks spatially and temporally. Moreover, we propose an enhanced video data preprocessing to enhance the training data regarding motion dynamics and prompt understanding, equipped with a new reference frame attention mechanism to improve the consistency of auto-regressive video generation. Extensive experiments demonstrate that our VideoTetris achieves impressive qualitative and quantitative results in compositional T2V generation. Code is available at: https://github.com/YangLing0818/VideoTetris

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Forward citations

Cited by 5 Pith papers

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

  1. TokensGen: Harnessing Condensed Tokens for Long Video Generation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    TokensGen generates consistent long videos by representing each clip as condensed semantic tokens, generating all tokens jointly from text, and stitching clips with adaptive FIFO denoising.

  2. CI-VID: A Coherent Interleaved Text-Video Dataset

    cs.CV 2025-07 conditional novelty 6.0 of 10

    CI-VID provides 341,550 interleaved text-video sequences with individual and transition captions and shows initial evidence that fine-tuning on them improves coherent multi-scene video generation.

  3. FreeLong++: Training-Free Long Video Generation via Multi-band SpectralFusion

    cs.CV 2025-06 conditional novelty 6.0 of 10

    FreeLong++ extends short-video diffusion models to 4x to 8x longer clips, without retraining, by fusing multiple windowed attention branches through frequency-domain filters and a spectral noise initialization.

  4. VCapsBench: A Large-scale Fine-grained Benchmark for Video Caption Quality Evaluation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    VCapsBench is a video caption quality benchmark with 109,796 QA pairs across 21 fine-grained dimensions on 5,677 videos, evaluating caption accuracy, inconsistency, and coverage.

  5. Captain Cinema: Towards Short Movie Generation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A two-stage text-to-movie system that plans keyframes for the story and then synthesizes video between them, using a compressed memory bank to keep long narratives consistent.

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