Open-source alternatives to RenderDiffusion

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

Modly is a local AI 3D model generator that converts two-dimensional images into three-dimensional meshes. It is a privacy-focused tool that processes data directly on the host graphics card using GPU-accelerated inference. The system serves as an extensible AI model framework, allowing the integration of external model extensions and runtime files from remote repositories. It utilizes a manifest-driven plugin architecture to add new generation methods by loading metadata and files from external version control systems. The toolset includes a command-line interface for triggering generation

Unity MCP is a plugin that connects the Unity Editor to AI assistants through the Model Context Protocol, enabling natural language control over scene manipulation, object creation, and editor workflows. It allows developers to generate C# scripts, modify GameObjects and components, create UI layouts, and manage assets by issuing commands through an AI interface, effectively turning the editor into a conversational development environment. The plugin distinguishes itself through a comprehensive automation system that can execute multi-step tasks from a design document, record and replay edito

Chen-Hsuan Lin, Chen Kong, and Simon Lucey AAAI Conference on Artificial Intelligence (AAAI), 2018

Codes for Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance (ECCV2020). paper

This is the PyTorch implementation of the ECCV 2022 paper MeshUDF. We provide one dummy pre-trained UDF network and code for demonstrating our differentiable meshing procedure of open surfaces.

This repository is the official implementation of UCLID-Net: Single View Reconstruction in Object Space (NeurIPS 2020).

The code for paper "Learning Implicit Fields for Generative Shape Modeling".

Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. This source code is licensed under the license found in the LICENSE file in the root directory of this source tree. -->

CO3Dv2: Common Objects In 3D (version 2)

Paper @ ArXiv, Video @ Youtube.

Code base of ParSeNet: ECCV 2020.

This is the implementation of NeurIPS 2020 paper "Generative 3D Part Assembly via Dynamic Graph Learning" created by Jialei Huang , Guanqi Zhan , Qingnan Fan , Kaichun Mo , Lin Shao , Baoquan Chen , Leonidas Guibas and Hao Dong .

This repository contains pre-trained models and testing code for MarrNet presented at NIPS 2017.

improves over nerf in 360 capture of unbounded scenes

DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction

Paper Code Demo Created by SerdarHELLI

Project page: http://cseweb.ucsd.edu/~viscomp/projects/CVPR20Transparent/ Dataset creation: https://github.com/lzqsd/TransparentShapeDatasetCreation Renderer: https://github.com/lzqsd/OptixRenderer Trained models: http://cseweb.ucsd.edu/~viscomp/projects/CVPR20Transparent/models.zip Synthetic…

This repository contains the code to reproduce the results from the paper.

Code repository for the paper: \ 3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View Marc Badger, Yufu Wang, Adarsh Modh, Ammon Perkes, Nikos Kolotouros, Bernd Pfrommer, Marc Schmidt, Kostas Daniilidis \ ECCV 2020 \ Project Page

Learnable Deformations Among 3D Shapes.

Tips: If you meet any problems when reproduce our results, please contact Yikang Ding (dyk20@mails.tsinghua.edu.cn). We are happy to help you solve the problems and share our experience.

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

This repository constains the codes and ShapeNet-Skeleton.zip datasets for the Paper.

Created by Muheng Li, Yueqi Duan, Jie Zhou, and Jiwen Lu.

by: Alexandre Boulch and Renaud Marlet

This is an implementation of the ICCV'19 paper "Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation".