LongVie 2: Multimodal, Controllable, Ultra-Long Video World Model
LongVie 2 extends the Wan2.1 diffusion backbone into an autoregressive video world model capable of generating coherent 3-to-5-minute video sequences.
Why it matters
LongVie 2 represents an important advancement in video generation, overcoming the temporal drift and inconsistencies that typically degrade long-horizon generations.
Key Points
- 1Integrates dense and sparse control signals to improve controllability
- 2Uses degradation-aware training to maintain high visual quality during long-term inference
- 3Employs history-context guidance to ensure temporal consistency across the video
- 4Supports continuous video generation lasting up to five minutes
Details
LongVie 2 is an end-to-end autoregressive framework that aims to build a video world model with three essential properties: controllability, long-term visual quality, and temporal consistency. The system achieves this through a three-stage pipeline. First, it anchors generation in strict geometry using multi-modal control signals like depth maps and motion vectors. Second, it employs degradation-aware training to teach the network how to self-repair quality loss during autoregressive inference. Finally, it uses history-context guidance to enforce logical continuity across video segments, preventing subject amnesia. These architectural changes, combined with training-free inference techniques, allow LongVie 2 to generate coherent, causally consistent video sequences up to five minutes long, marking a significant step toward unified video world modeling.
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