Novel View Synthesis with Pixel-Space Diffusion Models

Understand

Synthesizing a novel view from a single input image is a challenging task.

• Traditionally, this task was approached by estimating scene depth, warping, and inpainting, with machine learning models enabling parts of the pipeline.
• More recently, generative models are being increasingly employed in novel view synthesis (NVS), often encompassing the entire end-to-end system.
• In this work, we adapt a modern diffusion model architecture for end-to-end NVS in the pixel space, substantially outperforming previous state-of-the-art (SOTA) techniques.