GAD: Geometry Attention Distillation for Consistent Video Generation
cs.CV
Submitted: 2026-10-13
Updated: 2026-10-13
Comments: Project page: https://fantasy-amap.github.io/GAD/
Project page: https://fantasy-amap.github.io/GAD
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
- HunyuanVideo: A Systematic Framework For Large Video Generative Models
- Wan: Open and Advanced Large-Scale Video Generative Models
- Learning Interactive Real-World Simulators
- GAIA-1: A Generative World Model for Autonomous Driving
- SCoPE: Sightline-Coordinate Positional Encoding for Video Diffusion Transformers
- VGGT-$\Omega$
- LongLive: Real-time Interactive Long Video Generation
- Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
- Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation
- FreeSpec: Training-Free Long Video Generation via Singular-Spectrum Reconstruction
- Temporal State Transport in Video Generation: Diagnosing and Correcting Spectral Imbalance
- Memorize-and-Generate: Towards Long-Term Consistency in Real-Time Video Generation
- MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation
- Pack and Force Your Memory: Long-form and Consistent Video Generation
- MemFlow: Flowing Adaptive Memory for Consistent and Efficient Long Video Narratives
- DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory
- Towards Error-Free Long Video Generation
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