Towards Error-Free Long Video Generation

2026-06-21

AI Summary

Long-form video generation faces significant challenges, including error accumulation, attribute drift, and data scarcity, which hinder identity consistency and dynamic coherence. This work proposes a framework for generating videos of effectively unlimited length by fine-tuning diffusion models on short clips to enable autoregressive segment generation. To ensure both local fidelity and long-term consistency, the approach integrates an inter-segment causal attention mechanism that combines bidirectional and unidirectional attention during long-video training. Additionally, a Truncated Rectified Flow (T-RFlow) is introduced to suppress error propagation, while KV caching is employed to enhance inference efficiency. The method achieves, for the first time, high-fidelity, dynamically natural, and identity-consistent single-shot videos spanning multiple minutes, establishing a new state-of-the-art benchmark in long-form video synthesis.

Abstract

Recent advances in video generation have made minute-level synthesis possible; however, generating long videos remains challenging due to error accumulation, attribute drift, and the limited availability of long video data. In this paper, we introduce an infinite-length video generation framework that focusing on addressing these issues and produces high-quality, dynamic, and identity-consistent single-shot long videos. We first finetune a diffusion model as a video extension model on large-scale short video data to autoregressively generate temporally coherent clips. Inspired by the success of large language models (LLMs), we adopt causal attention computation between clips to further finetune this model on long video data. In this way, the tokens in one clip (short video) are computed by bidirectional attention while tokens among clips are computed by unidirectional attention. This design leverages the strengths of modern diffusion models while preserving long-term context information, effectively mitigating error accumulation and attribute drift. To achieve memory efficiency during inference, we adopt a key-value (KV) caching mechanism to maintain a constant KV memory. Furthermore, we introduce truncation-rectified flow (T-RFlow) technique to further suppress error accumulation. Experimental results demonstrate the effectiveness of our method. Our framework establishes a new benchmark for realistic and coherent minute-level video synthesis.

Problem

• error accumulation
• attribute drift
• long video generation
• temporal coherence
• identity consistency

Innovation

• causal attention
• diffusion model
• error accumulation mitigation
• KV caching
• truncation-rectified flow