You give it an image and it predicts, for every pixel, how much of that pixel belongs to the subject. Background pixels turn transparent, the subject stays solid, and boundary pixels like hair, fur, thin wires, and motion blur become translucent instead of being cut with scissors. That last part is the hard bit, and it is where this model earns its keep: windblown hair and bicycle wheels survive the cutout.
Under the hood it builds on BiRefNet, a well-regarded segmentation architecture, retrained by Feyn on a mix of data designed to balance two skills that usually fight each other: recognizing which pixels form the subject and tracing the boundary precisely. At about 260 million parameters it is compact by modern standards, and the weights are Apache licensed. By the team's own evaluation it posts the best published S-measure on four of eight standard benchmarks, including ultra high resolution images, and stays within two percent of the leader on the rest.