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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

Goku: Flow Based Video Generative Foundation Models

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
classification cs.CV
keywords generationgokumodelsflowimage-and-videojointperformancetasks
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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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Cited by 4 Pith papers

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  4. Evolution of Video Generative Foundations

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    This survey traces video generation technology from GANs to diffusion models and then to autoregressive and multimodal approaches while analyzing principles, strengths, and future trends.