TripoSG - High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models

TripoSG is a state-of-the-art image-to-3D generation foundation model that leverages large-scale rectified flow transformers to produce high-fidelity 3D shapes from single images.

Model Description

Model Architecture

TripoSG utilizes a novel architecture combining:

• Rectified Flow (RF) based Transformer for stable, linear trajectory modeling
• Advanced VAE with SDF-based representation and hybrid geometric supervision
• Cross-attention mechanism for image feature condition
• 1.5B parameters operating on 2048 latent tokens

Intended Uses

This model is designed for:

• Converting single images to high-quality 3D meshes
• Creative and design applications
• Gaming and VFX asset creation
• Prototyping and visualization

Requirements

• CUDA-capable GPU (>8GB VRAM)

Usage

For detailed usage instructions, please visit our GitHub repository.

About

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

• GitHub Repository
• Paper
• Gradio Demo