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sam-3d-body is a machine learning framework for 3D human mesh recovery and pose estimation. It utilizes a 3D human mesh recovery model to reconstruct full-body meshes, including the body, hands, and feet, from a single image. The project implements a specialized extension of the Segment Anything Model to guide the extraction and refinement of human body shapes. This integration allows for prompt-guided mesh recovery, where 2D masks and keypoints constrain the inference of 3D pose and shape parameters. The system covers a range of computer vision capabilities, including 3D spatial alignment t
DensePose is a 3D human pose estimation framework designed to map 2D image pixels to a 3D surface-based model of the human body in real time. It functions as a computer vision anatomical mapper that projects 2D visual data onto a 3D surface to create detailed anatomical representations. The system operates as an image-to-3D texture transfer engine, localizing 2D image annotations onto 3D models to apply photographic textures to digital human representations. It uses a surface-based body mapping method to associate human pixels in an RGB image with specific coordinates on a 3D body template.
SAM 3D Objects is a promptable foundation model that recovers 3D objects and human meshes from single images. It converts masked objects in a single photograph into full 3D models with pose, shape, texture, and layout, while also producing complete 3D human body meshes from the same input. The system integrates promptable segmentation to isolate objects and humans before reconstruction, then aligns the independently reconstructed 3D elements into a shared coordinate space. This enables scene-level understanding where multiple 3D reconstructions from the same image coexist in a common coordina
Grounded-Segment-Anything is a suite of specialized tools for multimodal visual analysis, text-based segmentation, and generative image editing. It integrates text-to-bounding-box detection and high-precision image segmentation masks to function as a text-based image segmenter and an automated visual labeling tool. The project enables text-driven image editing by identifying objects through natural language to perform inpainting and element replacement. It further extends visual analysis into three dimensions, allowing for 3D human reconstruction and the generation of 3D bounding boxes from t
AMASS is a large database of human motion unifying different optical marker-based motion capture datasets by representing them within a common framework and parameterization. AMASS is readily useful for animation, visualization, and generating training data for deep learning.
The main features of nghorbani/amass are: 3D Human Mesh Recovery.
Open-source alternatives to nghorbani/amass include: facebookresearch/sam-3d-body — sam-3d-body is a machine learning framework for 3D human mesh recovery and pose estimation. It utilizes a 3D human… facebookresearch/densepose — DensePose is a 3D human pose estimation framework designed to map 2D image pixels to a 3D surface-based model of the… facebookresearch/sam-3d-objects — SAM 3D Objects is a promptable foundation model that recovers 3D objects and human meshes from single images. It… idea-research/grounded-segment-anything — Grounded-Segment-Anything is a suite of specialized tools for multimodal visual analysis, text-based segmentation, and… geopavlakos/texturepose — [paper] [project page]. albertpumarola/3dpeople-dataset — First dataset of dressed humans with specific geometry representation for the clothes. It contains ~2 Million images…