LoL: Longer than Longer, Scaling Video Generation to Hour
Justin Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, Cho-Jui Hsieh

TL;DR
This paper introduces a novel, training-free method to prevent sink-collapse in long-form video generation models, enabling real-time, streaming, and infinite-length videos up to 12 hours with minimal quality loss.
Contribution
It identifies the cause of sink-collapse as a conflict between Rotary Position Embedding and multi-head attention, and proposes multi-head RoPE jitter to effectively mitigate this issue.
Findings
Successfully prevents sink-collapse in long videos
Enables real-time, streaming, and infinite-length video generation
Achieves up to 12 hours of continuous video with little quality decay
Abstract
Recent research in long-form video generation has shifted from bidirectional to autoregressive models, yet these methods commonly suffer from error accumulation and a loss of long-term coherence. While attention sink frames have been introduced to mitigate this performance decay, they often induce a critical failure mode we term sink-collapse: the generated content repeatedly reverts to the sink frame, resulting in abrupt scene resets and cyclic motion patterns. Our analysis reveals that sink-collapse originates from an inherent conflict between the periodic structure of Rotary Position Embedding (RoPE) and the multi-head attention mechanisms prevalent in current generative models. To address it, we propose a lightweight, training-free approach that effectively suppresses this behavior by introducing multi-head RoPE jitter that breaks inter-head attention homogenization and mitigates…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Human Pose and Action Recognition · Visual Attention and Saliency Detection
