---
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)