MatAnyone (CVPR 2025)
Source: https://github.com/pq-yang/matanyone
Structured fields
{
"archived": false,
"checked_at": "2026-10-17T00:00:00.000Z",
"usefulness": "Target-assigned video matting with consistent memory propagation (CVPR 2025): give a first-frame mask, get stable alpha through the clip. Superseded by MatAnyone 2.",
"source_type": "repository",
"availability": "available",
"source_owner": "Peiqing Yang et al. (S-Lab, NTU)",
"next_check_at": "2026-10-31T00:00:00.000Z",
"successor_url": "https://github.com/pq-yang/MatAnyone2",
"known_limitations": [
"Non-commercial license."
],
"upstream_updated_at": "2026-03-04T15:52:40.000Z"
}Evidence claims
- [sourced_fact] README header points to the new release MatAnyone 2; later updates (2026.03) add training code and evaluation scripts. · locator: README, top source
- [sourced_fact] README License: licensed under NTU S-Lab License 1.0; LICENSE grants redistribution and use for non-commercial purpose. · locator: README License; LICENSE header source
- [upstream_claim] Research review verdict at export: reviewed (as of 2026-10-17). Recorded by the research pipeline, not a hub review outcome; evidence.review stays unreviewed until an independent reviewer records one.
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| code | LicenseRef-NTU-S-Lab-1.0 | restricted | restricted | restricted | unknown | S-Lab License 1.0: redistribution and use for non-commercial purpose only, with attribution and no-endorsement conditions. |
| model_weights | unknown | unknown | unknown | unknown | unknown | README declares the project under S-Lab License; weights terms not separately verified. |