Supra-3D / SUPRA-Tex

SUPRA-Tex: 3D Generation with Super-Aligned Texture 
 English | 简体中文 
 Chen Yang 1* ,
 Jiemin Fang 1*†✉ ,
 Fanpeng Meng 1* ,
 Zihao Huang 1 , 
 Jichen Pang 2 ,
 Guanjun Wu 3 ,
 Zhikuan Bao 1 ,
 Qi Tian 1✉ 
 1 Huawei Inc.   
 2 Shanghai Jiao Tong University   
 3 Huazhong University of Science and Technology 
 * Equal contribution   
 † Project lead   
 ✉ Corresponding authors
https://github.com/user-attachments/assets/ecc9e9ec-e91f-4eba-927d-86322796459a
**SUPRA-Tex** generates textured 3D assets from a single image, preserving details
such as lettering, patterns, and material boundaries. It predicts PBR materials
in the reference view, encodes them as surface-aligned 3D observations, and
completes unseen regions with an **Interleaved Flow Transformer**. The output is
a relightable GLB with base color, metallic, and roughness textures.
- **Image-aligned textures.** Use visible surface observations to guide 3D
 material generation and retain the details of the input image.
- **Complete PBR assets.** Generate materials for unseen surfaces and export
 textures that support rendering under new lighting.
- **Image-to-3D and mesh texturing.** Generate an asset from an image, or texture
 an existing mesh with matching camera and material observations.
Learn more on the project page, or try the
online demo.
## Release Notes
- 🔥 **[2026-10-19]** Inference code released for image-to-3D and mesh texturing, along with a training overview.
- 🎨 **[2026-10-18]** The SUPRA-Tex Studio is open for a tour.
- 📄 **[2026-10-16]** Project page and paper released.
## Pretrained Models
Our models are openly available on Hugging Face.
| Model | Parameters | Download |
| --- | --- | --- |
| SUPRA-Tex 3D Completion | 2.1B | completion |
| SUPRA-Tex Material Adapter | 33.6M trainable | material |
## Installation
SUPRA-Tex requires **Linux** with an **NVIDIA GPU**, a **CUDA 12.4** toolkit with
`nvcc`, Git, and a C++ compiler. The installer sets up a dedicated Python 3.10
Conda environment with PyTorch 2.6.0 and the native CUDA extensions:
 [code] 
For faster attention, also install
FlashAttention: FlashAttention-2
on Ampere, Ada, or Hopper GPUs, and FlashAttention-3 on Hopper. Without it,
inference uses PyTorch SDPA; once installed, it is selected automatically. See the
installation guide
for the commands.
The full image-to-3D pipeline has been tested on an NVIDIA RTX 3090 (24 GB), where
CPU offload is enabled automatically. See the installation guide
for `venv` setup and manual installation, and
GPU compatibility for memory options.
## Quick Start
**1. Prepare the models.** The SUPRA-Tex models
need no access request, but the image-to-3D pipeline also uses Meta's
DINOv3, which
requires access approval. Request access on its model page, log in with an
approved account, then download and assemble all models into `ckpts/ready`:
 [code] 
By default the models are downloaded into `ckpts/ready`, which stays
self-contained. If your Hugging Face cache already holds some of them, add