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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
Wonder3D is a diffusion-based system for single image 3D reconstruction. It generates high-detail 3D meshes from a single input image by producing consistent multi-view normal maps and color images. The pipeline functions as a multi-view normal map generator and a textured mesh extractor. It uses cross-domain multi-view synthesis to create view-dependent maps, which are then converted into 3D geometry through radiance fusion and memory-efficient surface reconstruction. The project covers 3D mesh generation, multi-view generation, and textured 3D modeling. It also includes capabilities for tr
pifuhd is a 3D human reconstruction framework that generates high-resolution 3D meshes of people from a single 2D image. It utilizes pixel-aligned implicit functions to map image pixels to 3D space, predicting surface occupancy and distance to create detailed geometry. The system includes a pipeline for creating digital human assets, moving from 2D image feature projection to the extraction of discrete triangular meshes. It features specialized tools for refining these models, including a post-processor that removes geometric artifacts by isolating the largest connected component of the mesh.
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
vrn is a 3D face reconstruction tool that generates three-dimensional volumetric representations of human faces from single two-dimensional images. It utilizes a volumetric convolutional neural network regression model to predict 3D volume data directly from image pixels.
The main features of aaronjackson/vrn are: Single-Image 3D Reconstructions, Direct Voxel Prediction, Volumetric Regression Architectures, Volumetric CNN Regression, 3D Face Reconstruction, Volumetric Reconstruction Tools, Monocular Depth Estimators, Human Digitalization.
Projects with overlapping indexed features include: cleardusk/3ddfa — 3DDFA is a 3D face reconstruction tool that generates three-dimensional facial meshes and 68 structural landmarks from… xxlong0/wonder3d — Wonder3D is a diffusion-based system for single image 3D reconstruction. It generates high-detail 3D meshes from a… facebookresearch/pifuhd — pifuhd is a 3D human reconstruction framework that generates high-resolution 3D meshes of people from a single 2D… yadiraf/prnet — PRNet is a Python library for 3D facial reconstruction. It uses a deep learning regression model to predict 3D facial… nv-tlabs/get3d — GET3D is a generative 3D mesh model and rendering framework designed to synthesize high-quality textured shapes and… 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…