This is a simplified guide to an AI model called sam3-video maintained by lucataco. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter. This is a simplified guide to an AI model called sam3-video sam3-video maintained by lucataco lucataco . If you like these kinds of analysis, join AIModels.fyi AIModels.fyi or follow us on Twitter Twitter . Model overview sam3-video is a unified foundation model from Meta Research designed for prompt-based segmentation in both images and videos. The model accepts text descriptions, visual prompts like points and boxes, or object tracking across frames to identify and isolate specific elements within video content. This approach differs from earlier segmentation tools that often require manual mask creation or frame-by-frame annotation. The creator, lucataco, has implemented this model on Replicate for accessible video segmentation workflows. sam3-video lucataco For related work in this space, you may find Segment Anything 2 useful for automatic mask generation, while videollama3-7b provides multimodal video understanding capabilities. When you need to prepare video files for processing, trim-video and video-merge offer quick editing utilities. Segment Anything 2 videollama3-7b trim-video video-merge Model inputs and outputs The model takes video files and text prompts as primary inputs, then outputs segmented video with customizable visualization options. You can control mask appearance through color selection and opacity settings, and request either overlay videos or raw PNG mask sequences for further editing. Inputs video: The input video file to process (required)prompt: Text description of the object to segment, such as "person", "car", or "foot" (required)negative_prompt: Optional text to specify objects to exclude from segmentationvisual_prompt: Optional JSON string for advanced point-based or box-based promptsmask_color: Color of the segmentation overlay (green, red, blue, yellow, cyan, or magenta; default is green)mask_opacity: Opacity level from 0.0 to 1.0 (default 0.5)mask_only: Boolean to return high-contrast black and white masks instead of overlaysreturn_zip: Boolean to request a ZIP file containing individual PNG masks for every frame video: The input video file to process (required) video prompt: Text description of the object to segment, such as "person", "car", or "foot" (required) prompt negative_prompt: Optional text to specify objects to exclude from segmentation negative_prompt visual_prompt: Optional JSON string for advanced point-based or box-based prompts visual_prompt mask_color: Color of the segmentation overlay (green, red, blue, yellow, cyan, or magenta; default is green) mask_color mask_opacity: Opacity level from 0.0 to 1.0 (default 0.5) mask_opacity mask_only: Boolean to return high-contrast black and white masks instead of overlays mask_only return_zip: Boolean to request a ZIP file containing individual PNG masks for every frame return_zip Outputs Output: A video file with segmentation overlay or a ZIP archive containing the overlay video and frame-by-frame PNG masks Output: A video file with segmentation overlay or a ZIP archive containing the overlay video and frame-by-frame PNG masks Output Capabilities The model segments objects across video frames using natural language descriptions. You can specify "foot" to isolate footwear in sports footage, request "person" to track individuals through scenes, or use color and opacity controls to create specific visual styles. The model handles multi-frame consistency, maintaining object identification across temporal sequences. You can also extract raw binary masks for compositing in professional video editing software, enabling integration with After Effects or Premiere Pro workflows. What can I use it for? Video content creators can use this for automated object isolation in post-production, reducing manual rotoscoping hours significantly. Marketing teams can generate product highlights by segmenting specific items in demonstration videos. Film and television productions benefit from efficient tracking-based masking for visual effects compositing. Educational content creators can isolate anatomical structures or mechanical components in instructional videos. You can also license segmentation services to clients needing automated video processing, leveraging the text-prompt interface to offer accessible tools without requiring technical masking knowledge. Things to try Experiment with negative prompts to exclude similar objects—for example, prompt "person" with negative prompt "face" to segment only body portions. Generate multiple mask extractions with different opacity levels in a single batch, then composite them as layers for advanced color grading effects. Test visual prompts by providing point coordinates alongside text descriptions to refine segmentation boundaries when text alone produces imprecise results. Extract raw PNG masks from full videos, then use them to create custom color grades or selective blur effects in your editing software rather than relying on the built-in overlay colors.

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