Research Paper#Computer Vision, Video Processing, Diffusion Models🔬 ResearchAnalyzed: Jan 3, 2026 23:58
EasyOmnimatte: End-to-End Video Layered Decomposition with Diffusion Models
Analysis
This paper introduces EasyOmnimatte, a novel end-to-end video omnimatte method that leverages pretrained video inpainting diffusion models. It addresses the limitations of existing methods by efficiently capturing both foreground and associated effects. The key innovation lies in a dual-expert strategy, where LoRA is selectively applied to specific blocks of the diffusion model to capture effect-related cues, leading to improved quality and efficiency compared to existing approaches.
Key Takeaways
- •EasyOmnimatte is a novel end-to-end video omnimatte method.
- •It leverages pretrained video inpainting diffusion models.
- •The method uses a 'Dual-Expert' strategy with selective LoRA application.
- •It achieves state-of-the-art performance in video omnimatte.
- •The approach is more efficient than existing methods.
Reference
“The paper's core finding is the effectiveness of the 'Dual-Expert strategy' where an Effect Expert captures coarse foreground structure and effects, and a Quality Expert refines the alpha matte, leading to state-of-the-art performance.”