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Goku: Flow Based Video Generative Foundation Models

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arxiv 2502.04896 v2 pith:VO5PWDYV submitted 2025-02-07 cs.CV

Shoufa Chen , Chongjian Ge , Yuqi Zhang , Yida Zhang , Fengda Zhu , Hao Yang , Hongxiang Hao , Hui Wu
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Zhichao Lai Yifei Hu Ting-Che Lin Shilong Zhang Fu Li Chuan Li Xing Wang Yanghua Peng Peize Sun Ping Luo Yi Jiang Zehuan Yuan Bingyue Peng Xiaobing Liu
This is my paper · ORCID
classification cs.CV
keywords generationgokumodelsflowimage-and-videojointperformancetasks
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces Goku, a state-of-the-art family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance. We detail the foundational elements enabling high-quality visual generation, including the data curation pipeline, model architecture design, flow formulation, and advanced infrastructure for efficient and robust large-scale training. The Goku models demonstrate superior performance in both qualitative and quantitative evaluations, setting new benchmarks across major tasks. Specifically, Goku achieves 0.76 on GenEval and 83.65 on DPG-Bench for text-to-image generation, and 84.85 on VBench for text-to-video tasks. We believe that this work provides valuable insights and practical advancements for the research community in developing joint image-and-video generation models.

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Forward citations

Cited by 8 Pith papers

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

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    HuMo uses a two-stage training scheme and a face-focus trick to generate human videos that follow text, keep a reference person's identity, and sync speech to audio, beating several single-task systems on benchmarks.

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    Phantom-Data provides around one million cross-context, identity-consistent reference-video pairs for subject-to-video generation, and training on it improves prompt following and visual quality.

  4. M4V: Multimodal Mamba for Efficient Text-to-Video Generation

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    M4V shows a Mamba-based text-to-video model can roughly match attention-based PyramidFlow on VBench while cutting mixer-layer FLOPs by 45% at 768x1280.

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  8. Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation

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