GoKawiilWe’re introducing MultiMatte, a background removal model you can aim with words. MultiMatte keeps the object you name and removes everything else.
Try MultiMatte on your own images at usefeyn.com/multimatte.
MultiMatte is built on SAM 3 (Meta, 2025). We used low-rank fine-tuning to modify 19.49M of its 860M parameters. That update touches only 2.27% of the model weights, yet MultiMatte improves substantially on image segmentation. On the DIS-VD benchmark, it scores a 0.901 S-measure against SAM 3’s 0.667.
Prompt steering drag to compare input Prompt: the dog Prompt: the dog Prompt: the dog bowl Prompt: the jeans
From Segmentation to Matting
SAM 3 is a concept-promptable detector. Given a phrase, the model returns binary masks for every matching object. These masks classify each pixel as either inside or outside the object. This binary approach fails on fine or translucent elements with fuzzy boundaries, like hair or a blurred screen.
MultiMatte addresses this gap with alpha mattes. Instead of assigning a boolean to each pixel, an alpha matte assigns a continuous opacity value to every pixel. Fuzzy boundaries can then be described as they actually appear.
Alpha mattes earn their keep on benchmarks. Across five high-resolution DIS splits, SAM 3 scores between 0.649 and 0.703. MultiMatte scores between 0.893 and 0.923.
Training MultiMatte
SAM 3 is not one network but several aligned parts: a vision tower, a CLIP text tower, a geometry encoder, a detection encoder and decoder, and a mask decoder. The ability to aim the model with a phrase lives in the alignment between those parts. Our objective was to carry this ability over to matting.
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