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Tech riderRev. 20 Sept 2026

MatAnyone

Ch 146.1of 28 AI Video Background Removers
  1. 1Runs onself-hosted, Web
  2. 2CostsNot stated by the maker
  3. 3Foreground isolationYes
  4. 4Max export resolution1080p
  5. 5Video input formats.mp4, .mov, .avi
4 lines stated Written from the maker's own pages: github.com
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Overview

MatAnyone is ranked #14 of 28 in AI video background removers on Specifiction. It runs on Self-hosted, Web.

Compared on AI video background removers

Foreground isolation
Yesgithub.com
Max export resolution
1080pgithub.com
Video input formats
.mp4, .mov, .avigithub.com

Facts

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 · 4 Oct 2026
Inputs and outputs
Inference takes a video and its first-frame segmentation mask and outputs foreground and alpha videos.github.com · 4 Oct 2026
Multiple targets
The inference scripts support processing multiple targets by using separate masks.github.com · 4 Oct 2026
Interactive demo
The Gradio demo lets users upload a video or image and assign target masks with a few clicks; it can run on Hugging Face or locally.github.com · 4 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 · 4 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 · 4 Oct 2026
Video formats
The example inputs include MP4, MOV, and AVI video files.github.com · 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 · 4 Oct 2026
License
The project uses the S-Lab License 1.0, which permits non-commercial use; commercial use requires contacting the contributors.github.com · 4 Oct 2026
Security and trust
The project pages opened for this research do not state security certifications or compliance claims.github.com · 4 Oct 2026
Support
The repository invites questions by email at [email protected].github.com · 4 Oct 2026
Research context
The project page identifies MatAnyone as a CVPR 2025 paper and lists the authors’ affiliations as S-Lab at Nanyang Technological University and SenseTime Research.pq-yang.github.io · 4 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 · 4 Oct 2026

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