Awesome GitHub RepositoriesParametric Human Model Fitting

EasyMocap is a markerless 3D human motion capture system that recovers body, hand, and face poses from single or multi-view video without physical markers or suits. It uses parametric body models like SMPL, SMPL-X, and MANO, and leverages mirror reflections to resolve depth ambiguity in single-view pose estimation, improving accuracy by computing mirror surface normals from vanishing points. The system distinguishes itself through mirror-assisted depth disambiguation, enabling accurate 3D pose reconstruction from a single RGB image or video that includes a mirror reflection. It also supports

Champ est un système de vision génératif et un générateur image-à-vidéo contrôlable conçu pour l'animation d'images humaines. Il utilise un synthétiseur vidéo basé sur la diffusion et un guidage paramétrique 3D pour transformer une image de référence unique en une séquence cohérente de mouvement basée sur des données de pilotage externes. Le framework se distingue par un système de transfert de pose humaine qui emploie l'extraction paramétrique du corps 3D et l'alignement dans l'espace de coordonnées. Cela permet au modèle de mapper le mouvement d'une vidéo de pilotage vers une personne de référence en s'ajustant aux échelles corporelles et aux perspectives de caméra en utilisant des cartes de profondeur et de conditionnement sémantique. Le système couvre un large éventail de capacités incluant l'affinement des données de mouvement, le transfert de mouvement paramétrique et un pipeline d'entraînement de modèle de vision à deux étapes. Ces processus assurent la cohérence structurelle et la stabilité temporelle à travers les animations humaines générées.

VIBE is a 3D human pose estimation framework designed to reconstruct human body shapes and poses from video frames. It functions as a toolkit for predicting parameters of the SMPL human body model to generate 3D mesh sequences. The system includes a 3D motion data exporter to convert predicted pose sequences into standard 3D file formats for use in graphics and animation software. It also provides a structured training pipeline for preparing datasets and training models to estimate body shapes from images. Its capabilities cover computer vision for estimating body pose and shape, as well as

Viser is a Python 3D visualization framework and remote scene server that renders 3D primitives, point clouds, and meshes in a web browser. It functions as a server-client system that synchronizes scene state and camera poses to a web client via WebSockets. The framework provides specialized capabilities for robotics and computer vision, including a URDF robot visualizer for loading robot models and joint states, as well as a GPU-accelerated Gaussian splatting viewer for high-fidelity volumetric rendering. It also supports the visualization of human body models and skinned meshes for pose ana

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