LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D
  Generation
Yushi Lan Fangzhou Hong Shuai Yang Shangchen Zhou Xuyi Meng Bo Dai Xingang Pan Chen Change Loy
The field of neural rendering has witnessed significant progress with
advancements in generative models and differentiable rendering techniques.
Though 2D diffusion has achieved success, a unified 3D diffusion pipeline
remains unsettled. This paper introduces a novel framework called LN3Diff to
address this gap and enable fast, high-quality, and generic conditional 3D
generation. Our approach harnesses a 3D-aware architecture and variational
autoencoder (VAE) to encode the input image into a structured, compact, and 3D
latent space. The latent is decoded by a transformer-based decoder into a
high-capacity 3D neural field. Through training a diffusion model on this
3D-aware latent space, our method achieves state-of-the-art performance on
ShapeNet for 3D generation and demonstrates superior performance in monocular
3D reconstruction and conditional 3D generation across various datasets.
Moreover, it surpasses existing 3D diffusion methods in terms of inference
speed, requiring no per-instance optimization. Our proposed LN3Diff presents a
significant advancement in 3D generative modeling and holds promise for various
applications in 3D vision and graphics tasks.