title: Goku: Flow Based Video Generative Foundation Models
summary: 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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paper_title: Goku: Flow Based Video Generative Foundation Models
paper_summary: 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.
paper_ai_summary: Goku, a state-of-the-art family of joint image-and-video generation models using rectified flow Transformers, sets new benchmarks in text-to-image and text-to-video tasks.
paper_ai_keywords: rectified flow Transformers image-and-video generation models text-to-image generation text-to-video tasks GenEval DPG-Bench VBench