facebookresearch/DensePoseArchived
DensePose
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.
The framework covers human pose estimation, 3D human body reconstruction, and real-time motion tracking. These capabilities enable the mapping of human pixels from a 2D image to a 3D surface model to track movement and form.
Features
- 3D Human Mesh Recovery - Implements a framework for reconstructing 3D human bodies and meshes from 2D images in real time.
- 3D Pose Estimation - Analyzes 2D images to determine the precise 3D position and orientation of human body parts.
- Anatomical Mappers - Projects 2D visual data onto a 3D surface to create detailed anatomical representations of people.
- Convolutional Neural Networks - Implements a deep convolutional neural network to extract spatial features from RGB images for 3D coordinate prediction.
- Surface-Based Body Mapping - Associates every human pixel in an RGB image with a specific coordinate on a 3D body template.
- Anatomical Mesh Projections - Projects 2D pixel data onto a predefined 3D human anatomical template for consistent surface representation.
- Texture Transfer Pipelines - Localizes 2D image annotations onto 3D template models to apply photographic textures to digital human representations.
- Image-to-3D Texture Engines - Localizes 2D image annotations onto 3D models to apply photographic textures to digital human representations.
- Video-to-Surface Mapping - Maps human pixels from 2D images onto 3D geometry to track movement and form in real-time.
- Surface Coordinate Mapping - Maps 2D image pixels to a 3D body model using a coordinate system based on surface UV templates.
- Real-Time Motion Tracking - Processes RGB images instantly to map human movement onto a 3D model for interactive applications.
- Per-Pixel Reliability Scoring - Provides per-pixel confidence scores to filter out occlusions and background noise from the 3D mapping.
- Real-Time - Utilizes a high-speed inference runtime for low-latency mapping of 2D image frames to 3D surfaces.
- UV - Predicts exact pixel locations on a 3D mesh by regressing values within a normalized UV map.
- Human Body - Applies visual textures from 2D photographs onto a 3D digital body template for anatomical representations.
- Machine Learning Resources - A framework for mapping pixels to 3D surfaces.
- Pose Estimation Frameworks - Dense human pose estimation mapping pixels to 3D surfaces.
- Real-time Estimation - Dense human pose estimation in the wild.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
