BAgger: 自己回帰ビデオ拡散モデルのドリフトを後方集約で軽減
BAgger: Backwards Aggregation for Mitigating Drift in Autoregressive Video Diffusion Models
自己回帰ビデオ拡散モデルの誤差蓄積による品質ドリフトを、モデル自身のロールアウトから修正軌道を構築する自己教師あり学習で軽減する手法を提案。
著者: Ryan Po, Eric Ryan Chan, Changan Chen, Gordon Wetzstein
分類: cs.CV, cs.LG
原文アブストラクト
Autoregressive video models are promising for world modeling via next-frame prediction, but they suffer from exposure bias: a mismatch between training on clean contexts and inference on self-generated frames, causing errors to compound and quality to drift over time. We introduce Backwards Aggregation (BAgger), a self-supervised scheme that constructs corrective trajectories from the model's own rollouts, teaching it to recover from its mistakes. Unlike prior approaches that rely on few-step distillation and distribution-matching losses, which can hurt quality and diversity, BAgger trains with standard score or flow matching objectives, avoiding large teachers and long-chain backpropagation through time. We instantiate BAgger on causal diffusion transformers and evaluate on text-to-video, video extension, and multi-prompt generation, observing more stable long-horizon motion and better visual consistency with reduced drift.