3 papers
cs.CV2025
Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility
Yiheng Li, Feng Liang, Dan Kondratyuk +3
The substantial training cost of diffusion models hinders their deployment. Immiscible Diffusion recently showed that reducing diffusion trajectory mixing in the noise space via li…
cs.CV2025
MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation
Sihyun Yu, Meera Hahn, Dan Kondratyuk +6
Diffusion models are successful for synthesizing high-quality videos but are limited to generating short clips (e.g., 2-10 seconds). Synthesizing sustained footage (e.g. over minut…
cs.CV2024
CamViG: Camera Aware Image-to-Video Generation with Multimodal Transformers
Andrew Marmon, Grant Schindler, José Lezama +3
We extend multimodal transformers to include 3D camera motion as a conditioning signal for the task of video generation. Generative video models are becoming increasingly powerful,…