EgoLCD: Egocentric Video Generation with Long Context Diffusion
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.
693260ff6d1060ca587a2775
Liuzhou Zhang
693260ff6d1060ca587a2776
Jiarui Ye
693260ff6d1060ca587a2777
Yuanlei Wang
693260ff6d1060ca587a2778
Ming Zhong
693260ff6d1060ca587a2779
Mingju Cao
693260ff6d1060ca587a277a
Wanke Xia
693260ff6d1060ca587a277b
Bowen Zeng
693260ff6d1060ca587a277c
Zeyu Zhang
claimed_verified
2025-12-05T08:29:13.214Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
SteveZeyuZhang
user
SteveZeyuZhang
693260ff6d1060ca587a277d
Hao Tang
EgoLCD: Egocentric Video Generation with Long Context Diffusion
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.
EgoLCD addresses content drift in long egocentric video generation by integrating long-term sparse memory with attention-based short-term memory and structured narrative prompting, achieving state-of-the-art performance.
end-to-end framework long-term sparse KV Cache attention-based short-term memory LoRA Memory Regulation Loss Structured Narrative Prompting EgoVid-5M benchmark perceptual quality temporal consistency generative forgetting scalable world models embodied AI