What is videomama?

VideoMaMa is a tool that separates moving subjects from their backgrounds in videos, producing a clean foreground cutout (often called a matte) for each frame. Think of it as the AI equivalent of a green screen, but it works on any footage. You give it a video and a rough mask indicating what you want to keep, and it outputs a detailed, frame-by-frame separation that stays consistent over time. The tool works by combining two existing AI technologies. First, it uses a video generation model (Stable Video Diffusion) as a kind of prior, meaning it leverages what that model already knows about how objects and motion look in video. Second, it uses Meta's SAM2 to handle the mask tracking. The mask you provide acts as a guide, telling the system which subject to isolate, while the generative prior helps fill in fine details like hair, fur, or translucent edges that are notoriously difficult to cut out cleanly. This would be useful for video editors, visual effects artists, or content creators who need to pull subjects out of footage without a green screen. For example, a filmmaker could separate an actor from a cluttered background to place them in a new scene, or a marketer could isolate a product from a demo video for a composite ad. The community has already integrated the model into popular node-based tools like ComfyUI, suggesting a practical audience of hands-on video creators. The project is a research implementation tied to a CVPR 2026 paper, so it's primarily aimed at users comfortable with Python environments and command-line tools. There is a Hugging Face demo for trying it out without setup. Notably, the code is released under a non-commercial license, meaning it is free for research and personal use but requires separate permission for commercial products. The model weights themselves fall under a separate Stability AI license.