Goku: Flow Based Video Generative Foundation Models
This work addresses the problem of joint image-and-video generation for the research community, providing valuable insights and practical advancements.
The authors tackled the problem of joint image-and-video generation, achieving state-of-the-art performance with their Goku models, which scored 0.76 on GenEval, 83.65 on DPG-Bench, and 84.85 on VBench. This resulted in new benchmarks across major tasks.
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.