TripoSG: Generate High-Detail 3D Models from a Single Input Image 143

小编 · 2025-03-31

TripoSG is an open-source high-fidelity 3D shape synthesis technology developed by the VAST-ai-Research team. Built on a large-scale rectified flow architecture with a Mixture-of-Experts Transformer design, it generates high-detail 3D models from a single input image.

TripoSG: Generate High-Detail 3D Models from a Single Input Image

What is TripoSG?

TripoSG is an open-source high-fidelity 3D shape synthesis technology developed by the VAST-ai-Research team. Based on a large-scale rectified flow architecture and a Mixture-of-Experts (MoE) Transformer design, it can generate highly detailed 3D models from a single input image.

What is TripoSG?.webp

Key Features of TripoSG

High-fidelity generation: The generated 3D meshes have sharp geometric features, fine surface details, and complex structures.
Semantic consistency: The generated shapes accurately reflect the semantics and appearance of the input image.
Strong generalization: It can handle multiple input styles, including photorealistic images, cartoons, and sketches.
Robust performance: It can also create coherent shapes for challenging inputs with complex topological structures.

Technical Principles of TripoSG

Large-scale rectified flow Transformer: TripoSG is the first to apply a Transformer architecture based on Rectified Flow (RF) to 3D shape generation. Compared with traditional diffusion models, RF provides a simpler linear path modeling from noise to data, helping achieve more stable and efficient training.
Hybrid supervised training strategy: Combining signed distance function (SDF), normal, and Eikonal losses significantly improves the reconstruction performance of the 3D Variational Autoencoder (VAE).
High-quality data processing pipeline: A complete data construction and governance pipeline has been developed, including quality scoring, data filtering, repair and augmentation, SDF data production, and more. Through this pipeline, a dataset containing 2 million high-quality "image-SDF" training sample pairs has been built.
Efficient VAE architecture: Using SDF for geometric representation provides higher accuracy than the previously commonly used voxel occupancy grids.
MoE Transformer model: TripoSG is the first MoE Transformer model released in the 3D field.

Application Scenarios of TripoSG

Gaming and film/TV: Quickly generate game assets or 3D special effects elements, shortening development cycles.
Industry and e-commerce: Convert sketches or physical photos into 3D models for product preview or virtual display.
Education and cultural heritage: Create interactive teaching models or digitized cultural relics, lowering the barrier to 3D content creation.

Tutorial for Using TripoSG

1. Installation:

Clone the repository and create a conda environment:

git clone https://github.com/VAST-AI-Research/TripoSG.git
cd TripoSG
conda create -n tripoSG python=3.10
conda activate tripoSG

Install dependencies:

pip install torch torchvision --index-url https://download.pytorch.org/whl/{your-cuda-version}
pip install -r requirements.txt

2. Quick Start: Generate a 3D mesh from an image:

python scripts/inference_triposg.py --image-input assets/example_data/hjswed.png