High-Resolution 3D Assets Generation with Large Scale Hunyuan3D Diffusion Models.

From Images to High-Fidelity 3D Assets with Production-Ready PBR Material

[NeurIPS 2018] Visual Object Networks: Image Generation with Disentangled 3D Representation.

[NeurIPS 2023] Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation

[NeurIPS 2023] The repo of CommonScenes, a scene generation method powered by the diffusion model.

The code for CVPR2019 Oral paper "A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Images"

Torch implementation of our ICCV 17 paper: "3D-PRNN, Generating Shape Primitives with Recurrent Neural Networks"

Physically Compatible 3D Object Modeling from a Single Image

3D Shape Generation Baselines in PyTorch.

[3DV 2024] official repo of 3DV paper "RoomDesigner: Encoding Anchor-latents for Style-consistent and Shape-compatible Indoor Scene Generation"

CVPR2021 paper "Learning Parallel Dense Correspondence from Spatio-Temporal Descriptorsfor Efficient and Robust 4D Reconstruction"

Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences - CVPRW: StruCo3D, 2023

A Windows-compatible version of Hunyuan3D-2.1

This project is a DL model that performs 3D shape (voxelization) generation. It takes text prompts and produces 3D shapes. The model is implemented using supervised learning and has been tested on pytorch.

Super Random Polygon Generator

R package for generating synthetic shapes and images using the α-shape sampler

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Cube3D文本转3D模型软件免安装一键启动整合包