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

Paper recorded by Signals 4 on 2026-10-19 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-19 on arXiv · recorded by Signals 4 on 2026-10-20

Category: cs.CV · 计算机视觉 · first seen 2026-10-20

Abstract

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. However, the heavy compute of these diffusion backbones limits inference to a short chunk of just 6 to 24 frames, so even ordinary videos span multiple chunks. Because each chunk is matted

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#218 most recent of 756 cs.CV papers we have recorded · ↑ newer: Latent Bridge: From Video Latents to 3D Geometry via Feature Alignment · ↓ older: Ropedia W0: Scaling Loco-Manipulation World Models across Embodiments
Cite this page: VideoMaMa++: Temporally Consistent Video Matting via Preserve-and-Refine Token: the #218 most recent of 756 cs.CV papers we have recorded (as of 2026-10-19). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/videomama-temporally-consistent-video-matting-via-preserve-and-refine-token.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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