Core Concepts
Efficiently compressing 3D models into a low-dimensional triplane latent space for high-quality generation.
Abstract
Introduction to the challenges of 3D generation from single images.
Proposal of a triplane autoencoder for compressing 3D models efficiently.
Utilization of a diffusion model on the refined latent space.
Comparison with state-of-the-art algorithms, showcasing superior performance in less time and data.
Detailed explanation of the triplane encoder and decoder process.
Importance of shape embedding as an additional condition for accurate 3D generation.
Ablation studies on key designs like 3D-aware cross attention and diffusion prior model.
Extensive experiments, dataset curation, training details, comparison with other methods, and ablation studies conducted.
Stats
"Our approach uses latent diffusion models to generate 3D assets from a single image."
"Our method enables the generation of high-quality 3D assets in merely 7 seconds on a single A100 GPU."
Quotes
"Our approach achieves lower FID and higher CLIP similarity than Shap-E and OpenLRM."
"Our method can generate high-quality results under various viewing angles."