Reproduce “Seedance 2.0: Advancing Video Generation for World Complexity” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Team Seedance, De Chen, Liyang Chen, Xin Chen, Ying Chen, Zhuo Chen, Zhuowei Chen, Feng Cheng and 163 more
Seedance 2.0 accepts text, images, audio, and video as inputs, enabling creators to guide audiovisual generation and request editing, continuation, or extension. In SeedVideoBench 2.0, it supports 20 of 22 tested reference-to-video input/task variants and scores 3.75/5 for text-to-video motion quality, versus Kling 3.0’s 3.10/5. Video extension is a limitation: its task-following score is 1.93/3, below Veo 3.1’s 2.78/3.
Key insights
- A unified, large-scale architecture can effectively manage and synthesize diverse multi-modal inputs (text, image, audio, video) to produce highly coherent and realistic audio-visual content.
- Integrating advanced audio capabilities, such as binaural and multi-track generation with precise temporal alignment, is critical for achieving immersive and high-quality video output.
- Robust evaluation frameworks, combining expert subjective assessments and community-driven benchmarks, are essential for comprehensively validating performance in complex multi-modal generative tasks.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.