BioVid: Autoregressive Video Generation with Biological Behavior Semantic Comprehension
Video generation for biological behavior requires more than visually plausible motion: the duration of an action is itself a semantic property. Existing models usually rely on fixed temporal windows, external continuation, or prompt-driven stories, so length is specified externally rather than learned from behavior. To address this gap, we propose BioVid, a data-driven autoregressive framework for adaptive-length biological behavior generation. BioVid uses a 2D-encode/3D-decode tokenizer: a two-dimensional FSQ-R3GAN encoder converts each frame into discrete visual tokens, preserving single-frame information suited for next-token prediction and EOS-based termination, while a temporally inf...
| Authors | Tsung-Wei Pan, Jung-Hua Wang |
|---|---|
| Primary category | cs.CV |
| Published | 7 June 2026 |
| Last updated at source | 24 June 2026 |
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