VideoMaMa++: Temporally Consistent Video Matting via Preserve-and-Refine Token

Key points

• Because each chunk is matted independently from coarse mask guidance, existing methods fail to maintain consistency across chunk boundaries, producing flickering edges and discontinuous transparency.1
• We address this with a mask-guided diffusion-matting framework built on two ideas.1
• The RGB and mask conditioning are represented as separate token streams that interact through full spatio-temporal self-attention, letting the model adaptively combine fine RGB texture with coarse mask layout rather than committing to a fixed channel-wise fusion.1
• Across standard video matting benchmarks and in-the-wild long footage, our framework outperforms prior diffusion-based methods in both fine-detail accuracy and temporal consistency, and uniquely maintains consistency across chunk boundaries on long videos.1

Sources (2)

• [1]VideoMaMa++: Temporally Consistent Video Matting via Preserve-and-Refine Token
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 05:59 PM

  Diffusion-based video matting has recently emerged as a leading approach, leveraging video diffusion priors from internet-scale pretraining to achieve strong zero-shot generalization from limited synthetic data.

  Because each chunk is matted independently from coarse mask guidance, existing methods fail to maintain consistency across chunk boundaries, producing flickering edges and discontinuous transparency.

• [2]VideoMaMa++: Temporally Consistent Video Matting via Preserve-and-Refine Token
  Hugging Face Daily Papers · Oct 19, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.