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- MatAnyone
On the recordRead 9 Oct 202621 facts
MatAnyone
On the record
- Compute limit
- Users can set a maximum input size; videos exceeding it are downsampled, and no maximum is set by default.github.com · read 9 Oct 2026
- Inputs
- Inference takes a video and a segmentation mask for its first frame; documented video formats include MP4, MOV, and AVI.github.com · read 9 Oct 2026
- Inputs and outputs
- Inference takes a video and its first-frame segmentation mask and outputs foreground and alpha videos.github.com · read 4 Oct 2026
- Install
- The documented setup clones the repository, creates a Conda environment with Python 3.8, and installs dependencies with pip.github.com · read 9 Oct 2026
- Integration
- The README documents loading the model from Hugging Face for inference and identifies a Hugging Face integration.github.com · read 9 Oct 2026
- Integrations
- The project provides Hugging Face model loading and a Hugging Face demo, and references SAM2 as an example source of segmentation masks.github.com · read 4 Oct 2026
- Interactive demo
- The project offers a Gradio demo on Hugging Face and says it can also be launched locally, with target masks assigned through a few clicks.github.com · read 9 Oct 2026
- License
- The S-Lab License 1.0 permits redistribution and use for non-commercial purposes under stated conditions; commercial use requires contacting the contributors.github.com · read 9 Oct 2026
- Local setup
- The repository documents installation with Conda and Python 3.8, plus an optional dependency set for the Gradio demo.github.com · read 4 Oct 2026
- Multiple targets
- The README documents processing multiple targets by supplying a separate mask for each target.github.com · read 9 Oct 2026
- Outputs
- The inference results include a foreground video and an alpha video, with an option to save per-frame images.github.com · read 9 Oct 2026
- Product
- MatAnyone is a practical human video matting framework that supports assigning a target and produces stable core regions and fine-grained boundary details.github.com · read 4 Oct 2026
- Purpose
- MatAnyone is a human video matting framework designed for stable core-region semantics and fine-grained boundary details, with target assignment.github.com · read 9 Oct 2026
- Related release
- The repository points users to MatAnyone 2 as a newer release.github.com · read 9 Oct 2026
- Research context
- The repository identifies the work as a CVPR 2025 paper by researchers affiliated with NTU S-Lab and SenseTime Research.github.com · read 9 Oct 2026
- Research use
- The repository provides training instructions, evaluation scripts, benchmark data, and asks users to cite the CVPR paper when using the repository for research.github.com · read 4 Oct 2026
- Resolution handling
- Input resolution has no maximum by default, but users can set a maximum size that downsamples larger videos.github.com · read 4 Oct 2026
- Security
- The project pages opened do not state security or compliance certifications.github.com · read 9 Oct 2026
