TripoSG – Text to 3D Model

Summary

TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models TripoSG is an advanced high-fidelity, high-quality and high-generalizability image-to-3D generation foundation model. It leverages large-scale rectified flow transformers, hybrid supervised training, and a high-quality dataset to achieve state-of-the-art performance in 3D shape generation. ✨ Key Features High-Fidelity Generation : Produces meshes with sharp geometric features, fine surface details, and complex structures : Produces meshes with sharp geometric features, fine surface details, and complex structures Semantic Consistency : Generated shapes accurately reflect input image semantics and appearance : Generated shapes accurately reflect input image semantics and appearance Strong Generalization : Handles diverse input styles including photorealistic images, cartoons, and sketches : Handles diverse input styles including photorealistic images, cartoons, and sketches Robust Performance: Creates coherent shapes even for challenging inputs with complex topology 🔬 Technical Highlights Large-Scale Rectified Flow Transformer : Combines RF's linear trajectory modeling with transformer architecture for stable, efficient training : Combines RF's linear trajectory modeling with transformer architecture for stable, efficient training Advanced VAE Architecture : Uses Signed Distance Functions (SDFs) with hybrid supervision combining SDF loss, surface normal guidance, and eikonal loss : Uses Signed Distance Functions (SDFs) with hybrid supervision combining SDF loss, surface normal guidance, and eikonal loss High-Quality Dataset : Trained on 2 million meticulously curated Image-SDF pairs, ensuring superior output quality : Trained on 2 million meticulously curated Image-SDF pairs, ensuring superior output quality Efficient Scaling: Implements architecture optimizations for high performance even at smaller model scales 🔥 Updates [2025-03] Release of TripoSG 1.5B parameter rectified flow model...