GAD: Geometry Attention Distillation for Consistent Video Generation
Thus, we introduce Geometry Attention Distillation (GAD), which transfers correspondences from a frozen geometry model's attention to video attention.
ProofPaper ↗
Key points
- Video diffusion models generate visually compelling short-term sequences but often struggle to preserve scene geometry and object structure over time.
- In this paper, we first demonstrate that current video attention cannot identify reliable correspondences across frames, with responses dispersed across multiple regions even after recognizable scene structure has emerged.
- We argue that reliable cross-frame associations are essential for information exchange as scene structure forms in video generation.
- Experiments across all three video backbones demonstrate improved long-range scene consistency and dynamic temporal consistency, with geometric gains across generation lengths.
Sources (1)
- [1]GAD: Geometry Attention Distillation for Consistent Video GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:33 PM
Thus, we introduce Geometry Attention Distillation (GAD), which transfers correspondences from a frozen geometry model's attention to video attention.
Video diffusion models generate visually compelling short-term sequences but often struggle to preserve scene geometry and object structure over time.
Extractive summary: sentences quoted from the sources.
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