|
--- |
|
license: mit |
|
pipeline_tag: image-to-3d |
|
tags: |
|
- 3d-reconstruction |
|
- 3d-modeling |
|
- triposf |
|
- vae |
|
--- |
|
# TripoSF: High-Resolution 3D Shape Modeling with SparseFlex |
|
|
|
TripoSF is a state-of-the-art 3D shape modeling framework that enables differentiable mesh reconstruction at resolutions up to $1024^3$ directly from rendering losses. This repository contains the pretrained VAE model for high-fidelity 3D reconstruction. |
|
|
|
## Model Description |
|
|
|
TripoSF leverages a novel SparseFlex representation that combines the accuracy of Flexicubes with an efficient sparse voxel structure, focusing computation on surface-adjacent regions. |
|
|
|
### Key Features |
|
- 🔍 Ultra-high resolution reconstruction (up to $1024^3$) |
|
- 🎯 Direct optimization from rendering losses |
|
- 🌐 Natural handling of open surfaces and complex topologies |
|
- 💾 Memory-efficient sparse computation |
|
- 🔄 Differentiable mesh extraction with sharp features |
|
|
|
## Intended Uses |
|
|
|
This model is designed for: |
|
- High-fidelity 3D shape reconstruction |
|
- Mesh generation and modeling |
|
- 3D asset creation and optimization |
|
|
|
## Requirements |
|
|
|
- CUDA-capable GPU (≥12GB VRAM recommended for $1024^3$ resolution) |
|
- PyTorch 2.0+ |
|
|
|
## Usage |
|
|
|
For detailed usage instructions, please visit our [GitHub repository](https://github.com/VAST-AI-Research/TripoSF). |
|
|
|
## About |
|
|
|
TripoSF is developed by [Tripo](https://www.tripo3d.ai), [VAST AI Research](https://github.com/orgs/VAST-AI-Research), pushing the boundaries of 3D Generative AI. |
|
For more information: |
|
- [Project Page](https://xianglonghe.github.io/TripoSF/) |
|
- [Paper](https://arxiv.org/abs/2503.21732) |
|
- [GitHub Repository](https://github.com/VAST-AI-Research/TripoSF) |
