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.
About
TripoSF is developed by Tripo, VAST AI Research, pushing the boundaries of 3D Generative AI. For more information:
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