EgoLCD: Egocentric Video Generation with Long Context Diffusion

• URL: http://arxiv.org/abs/2512.04515v1
• Date: Thu, 04 Dec 2025 06:53:01 GMT
• Title: EgoLCD: Egocentric Video Generation with Long Context Diffusion
• Authors: Liuzhou Zhang, Jiarui Ye, Yuanlei Wang, Ming Zhong, Mingju Cao, Wanke Xia, Bowen Zeng, Zeyu Zhang, Hao Tang,
• Abstract summary: EgoLCD is an end-to-end framework for egocentric long-context video generation.<n>It combines a Long-Term Sparse KV Cache for stable global context with an attention-based short-term memory.<n>EgoLCD achieves state-of-the-art performance in both perceptual quality and temporal consistency.
• Score: 11.039806330368153
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generating long, coherent egocentric videos is difficult, as hand-object interactions and procedural tasks require reliable long-term memory. Existing autoregressive models suffer from content drift, where object identity and scene semantics degrade over time. To address this challenge, we introduce EgoLCD, an end-to-end framework for egocentric long-context video generation that treats long video synthesis as a problem of efficient and stable memory management. EgoLCD combines a Long-Term Sparse KV Cache for stable global context with an attention-based short-term memory, extended by LoRA for local adaptation. A Memory Regulation Loss enforces consistent memory usage, and Structured Narrative Prompting provides explicit temporal guidance. Extensive experiments on the EgoVid-5M benchmark demonstrate that EgoLCD achieves state-of-the-art performance in both perceptual quality and temporal consistency, effectively mitigating generative forgetting and representing a significant step toward building scalable world models for embodied AI. Code: https://github.com/AIGeeksGroup/EgoLCD. Website: https://aigeeksgroup.github.io/EgoLCD.

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