Projects sharing features with AaronJackson Vrn

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

GET3D is a generative 3D mesh model and rendering framework designed to synthesize high-quality textured shapes and tetrahedral meshes. It functions as an image-to-3D reconstructor and text-to-3D generator, utilizing a differentiable 3D renderer to produce realistic visual perspectives and material effects. The system enables the creation of 3D assets from single 2D images, point clouds, or descriptive text prompts. It features a latent space interpolator for creating smooth transitions between different 3D objects and supports the independent control of geometry and texture. The project cov

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

This project is a computer vision library designed for facial landmark detection and alignment. It provides a framework for identifying and mapping specific points on a human face in both two-dimensional and three-dimensional space, enabling the normalization of facial geometry and orientation across diverse images. The system utilizes a deep learning approach to extract precise facial coordinates, supporting tasks such as expression analysis and geometric modeling. By employing a stacked hourglass architecture, the model performs multi-stage feature refinement to capture spatial relationship

Dream Textures is a Stable Diffusion integration for Blender that provides tools for text-to-image generation, depth projection, and node-based processing within a 3D environment. It functions as an AI texture generator capable of producing image textures and concept art from text prompts and scene renders. The system features a depth-to-image projection tool that maps generated imagery onto 3D models using depth data for spatial alignment. It also includes a node-based AI image processor for creating procedural visual effects and a dedicated toolset for AI-assisted inpainting and outpainting

ComfyUI-3D-Pack is a suite of custom nodes for ComfyUI that enables 3D asset generation and rendering within a node-based workflow. It provides a set of tools for reconstructing textured three-dimensional meshes and volumetric scenes from single images, multi-view images, or text prompts. The system includes a Gaussian splatting generator for creating high-fidelity volumetric 3D scene representations and a multi-view image generator to produce consistent image sets for reconstruction. It also features a single image 3D mesh tool to build geometry from a single 2D source. The toolset covers 3

This is an official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision. The project was formerly referred by RingNet. The codebase consists of the inference code, i.e. give an face image using this code one can generate a 3D mesh of a…

This project is created by Tu Xiaoguang (xguangtu@outlook.com) and Luo Yao (luoyao_alpha@outlook.com). Any questions pls open issues for our project, we will reply quickly.

This project implements some basic functions related to 3D faces.

This project is a framework for neural radiance fields used to synthesize three-dimensional environments from sets of two-dimensional images and camera poses. It functions as a volumetric rendering engine and scene synthesizer that optimizes neural representations of spatial volumes to generate novel views of complex 3D scenes. The system implements a coordinate encoding system that transforms spatial coordinates into high-dimensional space to capture high-frequency geometric details. It also includes a neural mesh extractor that converts trained radiance fields into triangle meshes via march

ml-sharp is a neural radiance field framework designed for single-image 3D reconstruction. It uses a neural network to predict 3D geometry and appearance from a single photograph in a single feedforward pass. The system generates metric 3D scene representations and includes a real-time view synthesizer for producing high-resolution images of new viewpoints. It also features a camera trajectory renderer that creates video sequences by moving a virtual camera through the predicted 3D space. The project covers coordinate-based neural rendering, 3D Gaussian representation regression, and real-ti

TripoSR is a single-image 3D reconstruction system that generates a high-quality textured mesh from one photograph in under half a second. It uses a feedforward neural network to process a single image through a transformer architecture, compressing the input into a compact latent vector that conditions the entire reconstruction pipeline. The system outputs a separate UV texture map with configurable resolution, replacing vertex colors for higher-quality surface detail. The project is built around an end-to-end differentiable pipeline that trains the entire reconstruction system from image in

Hunyuan3D-2.1 is a generative 3D framework and image-to-3D pipeline that transforms single 2D images into textured 3D geometries. It functions as an asset generator that produces high-quality 3D meshes and textures using a flow-matching system. The project includes a specialized synthesizer for creating photorealistic textures with physically based rendering properties. These tools allow for the simulation of metallic reflections and light interactions on generated models. The system covers 3D asset pipeline automation through a sequence of shape generation and mesh refinement. It also provi

Point-e is a system for 3D model synthesis that generates three-dimensional point clouds from natural language descriptions and two-dimensional images. It utilizes diffusion models to synthesize these spatial representations based on text prompts or source images. The project includes specialized tools for refining these outputs, such as a point cloud upsampler to increase the density and resolution of low-resolution models. It also provides a mesh converter that uses distance function regression to transform raw point cloud data into structured 3D meshes. The broader capability surface cove

InstantMesh is a neural 3D reconstruction tool and single-image 3D mesh generator. It utilizes a sparse-view large reconstruction model to convert a single two-dimensional image into a three-dimensional object mesh. The system functions as a textured 3D mesh exporter, saving generated objects with either vertex colors or full texture maps for use in external rendering software. The framework covers a range of capabilities including feed-forward geometry inference, single-image depth estimation, and neural radiance fields. It also supports differentiable mesh rendering and workflows for spars

Threestudio is a 3D generative AI framework designed to create three-dimensional assets from text prompts and images. It provides specialized pipelines for text-to-3D generation and image-to-3D reconstruction, utilizing a neural radiance field trainer to produce geometry and textures. The framework is distinguished by its support for hybrid geometry backends, including signed distance functions, tetrahedra grids, and volume grids. It employs score distillation sampling to guide the generation process and features a modular plugin system for loading custom modules and nodes. The system covers

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

gsplat is a high-performance differentiable rasterization engine for 3D Gaussian splatting, designed for real-time novel view synthesis from 2D images. It provides a complete pipeline for reconstructing 3D scenes by optimizing differentiable Gaussian representations, training models from COLMAP-processed captures or proprietary device files, and generating new viewpoints through a CUDA-accelerated rendering backend. The framework distinguishes itself through memory-optimized CUDA kernels that reduce training memory usage by up to 4x compared to standard implementations while matching publishe

This project is a computer vision system for monocular depth estimation and 3D point cloud generation. It provides a supervised depth learning framework and a depth predictor capable of estimating spatial distance and disparity from single 2D images using pretrained neural networks. The system includes tools to transform 2D depth images into 3D point clouds via pixel coordinate backprojection and converts 3D point cloud data into 2D depth maps. It utilizes a training pipeline that supports model fine-tuning and hyperparameter optimization. The library covers broader capabilities in spatial a

This project is a monocular depth estimation model and computer vision framework designed to calculate absolute distance and scale from single images. It functions as a metric depth estimator that generates high-resolution depth maps without requiring camera-specific focal length metadata. The system utilizes a vision transformer architecture for feature extraction and zero-shot inference to produce metric-scale depth predictions. It includes specialized components for sharp-boundary depth refinement to maintain high-frequency edge details and prevent blurriness at object boundaries. The rep

Depth-Anything-V2 is a computer vision foundation model designed for general-purpose spatial understanding and depth perception. It functions as a monocular depth estimation model that predicts relative and absolute depth maps from single images or video sequences. The project provides specialized tools for both relative depth estimation and metric depth calculation, allowing for the determination of absolute physical distances in indoor and outdoor environments. It includes a video depth estimation framework that ensures temporal consistency across sequential frames to maintain stable depth

Depth-Anything-3 is a collection of core model implementations for depth prediction, multi-view geometry estimation, and RGB-D spatial pipelines. It includes a monocular depth estimation model for predicting depth maps from single images or video, and a 3D Gaussian splatting generator that predicts parameters to synthesize high-fidelity novel views of a scene. The project provides a multi-view geometry estimator for calculating spatially consistent depth and camera poses across synchronized visual inputs. It also functions as a visual SLAM enhancement tool designed to reduce drift and improve

VGGT is a computer vision framework designed for neural scene reconstruction and 3D environmental modeling. It utilizes a feed-forward neural architecture to process input images, simultaneously inferring camera parameters, depth maps, and point trajectories to generate dense 3D point clouds. The system distinguishes itself by integrating multi-view geometry with temporal tracking, allowing it to maintain spatial consistency across sequential frames. By leveraging pretrained neural backbones, the framework extracts robust visual features that support complex geometric tasks, including the ana

Depth-Anything is a monocular depth estimation foundation model that produces dense per-pixel depth maps from a single RGB image. It is built on a DINOv2 Vision Transformer encoder backbone and trained on 62 million unlabeled images using a teacher-student pseudo-labeling framework, enabling robust generalization across diverse scenes without task-specific training. The model outputs both relative depth maps, which capture the ordering of scene points, and metric depth maps with real-world units after fine-tuning on datasets like NYUv2 or KITTI. The project distinguishes itself through its ab

Neuralangelo is a neural surface reconstruction framework that transforms two-dimensional image sequences and multi-view photography into high-fidelity 3D meshes. It implements a pipeline for training neural radiance fields to represent complex scenes as digital geometry. The project utilizes a signed distance function for surface representation and multi-resolution hash encoding to capture both coarse and fine geometric details. It employs differentiable volume rendering and gradient-based eikonal regularization to ensure the learned distance functions remain physically plausible. The syste

libigl is a C++ geometry processing library used for analyzing and manipulating 3D triangle and tetrahedral meshes. It functions as a numerical linear algebra suite and a mesh manipulation framework, integrating a geometric deformation engine to implement rigid and polyharmonic transformations. The project is distinguished by its header-only library design and its implementation of specialized deformation techniques, including rigid-as-possible and polyharmonic shape deformation. It also provides a visualization tool for rendering surfaces and scalar fields with interactive scene controls and

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.