BiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression
Interactive video world models commonly convert bidirectional video generators into causal autoregressive systems through control fine-tuning, autoregressive training, causal initialization, and few-step distillation. This pipeline is costly, while frozen causal histories accumulate errors that degrade long-horizon fidelity and controllability. We present BiWM, the first open-source full-stack training framework for bidirectional autoregressive video world models. BiWM retains full attention within each generated chunk and requires only two stages: camera/action-control fine-tuning and few-step Distribution Matching Distillation (DMD). Both stages converge within a few hundred optimizer s...
| Authors | Shaohao Rui, Xiaofeng Mao, Zhanyu Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 June 2026 |
| Last updated at source | 24 July 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.