IDEA-Research/Grounded-Segment-Anything

Features

• Language-Based Segmentation - Provides high-precision image segmentation by interpreting natural language prompts to isolate specific objects.
• Multimodal Analysis Tools - Provides a framework that processes text, audio, and images to perform object detection and 3D mesh estimation.
• Text-Based Object Localization - Maps natural language descriptions to specific 2D spatial coordinates to locate objects in an image.
• 3D - Extends two-dimensional segmentation masks into three-dimensional bounding boxes by projecting image coordinates.
• Text-Prompted Masking - Produces precise masks for objects described in text by combining object detection with segmentation.
• Prompt-Based Masking - Generates precise pixel-level segmentation masks by feeding grounding coordinates into a pre-trained foundation model.
• Multimodal AI Pipeline Orchestration - Chains together speech-to-text, object detection, and segmentation models into a unified multimodal processing chain.
• Vision-Language Grounding Models - Implements a pipeline that maps natural language prompts to spatial bounding boxes for object grounding.
• Image Editing - Replaces or modifies specific objects in a visual asset by identifying them through text and applying inpainting.
• Generative Image Editing Tools - Enables text-driven object identification and replacement using inpainting and latent diffusion models.
• 3D Bounding Box Generation - Extends 2D segmentation prompts into a 3D environment to produce three-dimensional object bounding boxes.
• Conversational Interfaces - Implements a conversational interface allowing users to describe images, detect objects, and replace elements via a chatbot.
• Text-Guided Inpainting - Substitutes target objects identified by text with new generated objects using diffusion-based inpainting.
• Visual Conversational Analysis - Uses a chat interface to identify, describe, and label objects within images based on natural language prompts.
• Zero-Shot Inference - Identifies and isolates arbitrary objects without requiring class-specific training by leveraging pre-trained weights.
• 3D Human Mesh Recovery - Tracks people in images using text prompts to recover their full 3D body pose and shape.
• Human Reconstruction - Recovers a person's full 3D body pose and shape by tracking them in an image via a text prompt.
• Automated Labelers - Automatically generates bounding boxes and masks for large image datasets to create pseudo labels.
• Model-Assisted Labelers - Automatically creates image pseudo-labels, bounding boxes, and masks using recognition and captioning models.
• Latent Inpainting Masks - Replaces identified image regions with new content by masking segments and sampling from a latent diffusion model.

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