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arXiv · 2610.35724

MatAnyone-E: Sparse Memory Propagation for Efficient High-Resolution Video Matting

Shuyun Wang·Shangchen Zhou·Peiqing Yang·Yuekun Dai·Xin Yu·Chen Change Loy
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Abstract

High-resolution video matting requires preserving fine details, such as hair strands and soft transparency, while maintaining consistency across frames. Memory-based methods achieve strong matting quality by matching current-frame features with information stored from earlier frames, but this process becomes increasingly costly as resolution grows. We present MatAnyone-E, an efficient extension of MatAnyone 2 that focuses computation where it is most needed. Our approach first performs low-resolution propagation to identify uncertain regions that require closer inspection and the memory candidates most relevant to them. It then performs high-resolution matching only for these regions using the selected memory, while reusing low-resolution matching results elsewhere. This strategy reduces both the number of locations requiring detailed matching and the amount of memory searched, while preserving the information needed to produce a complete matte. We further reduce the cost of generating high-resolution mattes with a lightweight decoder designed around measured runtime bottlenecks and trained to reproduce the original decoder's representations. Experiments show that MatAnyone-E maintains matting quality comparable to MatAnyone 2 while achieving an estimated 7.4x inference speedup and reducing peak GPU memory usage to 7.6% of the usage of MatAnyone 2 at 4K. With compilation-enabled inference on a single NVIDIA H200 GPU, it reaches 73.07 FPS at 1080p and 28.62 FPS at 4K.

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Shuyun Wang, Shangchen Zhou, Peiqing Yang, Yuekun Dai, Xin Yu, Chen Change Loy. 2026-10-26. MatAnyone-E: Sparse Memory Propagation for Efficient High-Resolution Video Matting. https://arxiv.org/abs/2610.35724

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