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PRNet is a deep learning project for 3D face reconstruction and dense face alignment from a single image. Its core is an encoder-decoder network that maps a face photo to a UV position map encoding 3D geometry, from which the full shape and color of the face are recovered and saved as a mesh file, with optional front-view alignment.
The main features of yfeng95/prnet are: Single-Image 3D Reconstructions, Facial Landmark Analysis, Position Map Regression Networks, Face Alignment, Head Pose Estimation, Efficient Backbone Training, Mesh Exporters, Textured Face Mesh Exporters.
Projects with overlapping indexed features include: yadiraf/prnet — PRNet is a Python library for 3D facial reconstruction. It uses a deep learning regression model to predict 3D facial… cleardusk/3ddfa — 3DDFA is a 3D face reconstruction tool that generates three-dimensional facial meshes and 68 structural landmarks from… 1adrianb/face-alignment — This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a… vt-vl-lab/3d-photo-inpainting — This project is an RGB-D image inpainting tool and framework for 3D photo reconstruction. It transforms single 2D… alievk/avatarify-python — Avatarify-python is a real-time face animation tool that uses a PyTorch-based neural network to map facial movements… threestudio-project/threestudio — Threestudio is a 3D generative AI framework designed to create three-dimensional assets from text prompts and images.…
PRNet is a Python library for 3D facial reconstruction. It uses a deep learning regression model to predict 3D facial geometry and vertex colors from a single 2D input image to generate a textured mesh. The project provides tools for digital face swapping, allowing the replacement of a target face with a new image and blending textures to match the original pose. It also includes a framework for face texture swapping and blending to fit specific 3D poses. Additional capabilities cover facial analysis, including the detection and alignment of facial landmarks and the estimation of head pose a
3DDFA is a 3D face reconstruction tool that generates three-dimensional facial meshes and 68 structural landmarks from a single two-dimensional input image. The project provides utilities for estimating facial pose and depth maps to determine the orientation and position of a face. It includes a geometry exporter to save reconstructed facial shapes into standard file formats for use in external 3D modeling software. The software further covers automated face cropping using landmark-based and landmark-free identification methods, as well as the extraction of specialized mathematical represent
This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p
This project is an RGB-D image inpainting tool and framework for 3D photo reconstruction. It transforms single 2D images into 3D content by estimating monocular depth and synthesizing missing color and depth data to fill occluded regions. The system uses a layered depth image representation to manage scene boundaries and pixel connectivity. This allows for novel view synthesis, enabling the generation of videos that simulate motion parallax effects from different camera perspectives. The project covers a range of spatial modeling capabilities, including depth map estimation, disparity-based