LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation

arXiv:2403.12019v1 Announce Type: new
Abstract: 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. …

abstract arxiv cs.cv differentiable diffusion fields framework gap generative generative models neural rendering novel paper pipeline progress rendering scalable success type