Recast the Past: Stable Long Video Generation with Full Context Memory
Long-horizon video generation requires retaining an ever-expanding history, but conditioning on its growing token sequence is costly.
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Key points
- Existing methods restrict or compress historical context, potentially losing information needed for future generation.
- This work proposes ReCam, which groups local spatiotemporal blocks of historical latents into vectors and projects them directly into the high-dimensional hidden space of a Diffusion Transformer.
- The model learns to use the resulting memory tokens through native self-attention, reducing token count while retaining block-level access to the full history.
- To mitigate temporal drift during autoregressive rollout, we further introduce dual-pointer denoising with separate noise schedules for recent context and target frames.
Sources (1)
- [1]Recast the Past: Stable Long Video Generation with Full Context MemoryarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 10:27 AM
Long-horizon video generation requires retaining an ever-expanding history, but conditioning on its growing token sequence is costly.
Existing methods restrict or compress historical context, potentially losing information needed for future generation.
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