vivoCameraResearch/SDMatte
Official code for our ICCV2025 paper "SDMatte: Grafting Diffusion Models for Interactive Matting"
This project helps graphic designers, photographers, and marketers precisely cut out objects from images, even those with intricate edges like hair or fur. You provide an image and simple visual cues (like points, boxes, or rough masks), and the system outputs a perfectly matted image with the background removed. It's designed for anyone needing high-quality image cutouts for compositing, product showcases, or creative projects.
178 stars. No commits in the last 6 months.
Use this if you need to extract foreground objects with exceptional detail from complex backgrounds in a user-friendly, interactive way.
Not ideal if you need fully automated background removal without any user interaction or if you're working with video rather than still images.
Stars
178
Forks
7
Language
Python
License
MIT
Category
Last pushed
Aug 21, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/vivoCameraResearch/SDMatte"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
cswry/SeeSR
[CVPR2024] SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
JJLibra/SALAD-Pan
🤗 Official implementation for "SALAD-Pan: Sensor-Agnostic Latent Adaptive Diffusion for...
open-mmlab/mmgeneration
MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.
Janspiry/Image-Super-Resolution-via-Iterative-Refinement
Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch
hanjq17/Spectrum
[CVPR 2026] Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration