LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation

Understand

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.