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MatAnyone-E keeps the architecture of MatAnyone 2 and replaces the two parts whose cost grows fastest with resolution: - Sparse Memory Propagation.
MatAnyone-E (Shuyun-Wang/MatAnyone-E)
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MatAnyone-E README
The project's README from github.com/Shuyun-Wang/MatAnyone-E. As one markdown file: README.md.
Sparse Memory Propagation for Efficient High-Resolution Video Matting
1The University of Queensland
2S-Lab, Nanyang Technological University
3University of Adelaide
†Project lead *Corresponding authors
†Project lead *Corresponding authors
MatAnyone-E mattes native 4K video at 28.6 FPS on a single H200 (with torch.compile), 12× faster than MatAnyone 2, using 3.1 GB of GPU memory, while keeping fine details such as hair strands.
:movie_camera: More results on the project page.
📮 Update
- [2026.10] Project page and repository online.
🏄🏻♀️ TODO
- Paper on arXiv
- Inference code and checkpoints
- Hugging Face demo
- ComfyUI custom node
🔎 Overview
MatAnyone-E keeps the architecture of MatAnyone 2 and replaces the two parts whose cost grows fastest with resolution:
- Sparse Memory Propagation. A low-resolution pass picks the uncertain query locations (query pruning) and the memory most relevant to them (memory pruning); high-resolution matching runs only on those pairs.
- Lightweight decoder. Designed from stage-wise latency profiling and trained by feature distillation from the original decoder.
⚡ Efficiency
FPS and peak GPU memory on one NVIDIA A800-SXM4-80GB (fp16); (C.) = with torch.compile.
| Resolution | MatAnyone 2 | Ours | Ours (C.) | MatAnyone 2 memory | Ours memory | Ours (C.) memory |
|---|---|---|---|---|---|---|
| 1024×1024 | 29.5 | 41.3 | 47.3 | 1.60 GB | 0.92 GB | 0.61 GB |
| 1920×1080 | 13.6 | 26.6 | 37.6 | 5.47 GB | 1.66 GB | 1.07 GB |
| 2560×1440 | 5.6 | 16.5 | 25.1 | 16.20 GB | 3.04 GB | 2.02 GB |
| 3840×2160 | ~1.1 | 8.1 | 13.6 | ~79.69 GB | 6.08 GB | 3.89 GB |
~ extrapolated (a full memory bank does not fit in 80 GB). On one H200 with torch.compile: 73.07 FPS at 1080p, 28.62 FPS at 4K.
📑 Citation
If you find our repo useful for your research, please consider citing our paper:
@article{wang2026matanyonee,
title = {{MatAnyone-E}: Sparse Memory Propagation for Efficient High-Resolution Video Matting},
author = {Shuyun Wang and Shangchen Zhou and Peiqing Yang and Yuekun Dai and Xin Yu and Chen Change Loy},
journal = {arXiv preprint arXiv:2610.35724},
year = {2026}
}
📝 License
This project is licensed under NTU S-Lab License 1.0. Redistribution and use should follow this license.
👏 Acknowledgement
This project is built upon MatAnyone 2 and MatAnyone. Thanks for their awesome works!
📧 Contact
If you have any questions, please feel free to reach us at [email protected].