Deep neural networks (DNNs) have shown remarkable performance improvements on
vision-related tasks such as object detection or image segmentation. Despite their success …

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule
on the learned gradients, and back-propagate the score of a diffusion model through the …

Denoising diffusion models (DDMs) have shown promising results in 3D point cloud
synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high …

We present an efficient method for joint optimization of topology, materials and lighting from
multi-view image observations. Unlike recent multi-view reconstruction approaches, which …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

Neural signed distance functions (SDFs) are emerging as an effective representation for 3D
shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural …

Deep learning has significantly improved 2D image recognition. Extending into 3D may
advance many new applications including autonomous vehicles, virtual and augmented …

Reconstruction and intrinsic decomposition of scenes from captured imagery would enable
many applications such as relighting and virtual object insertion. Recent NeRF based …

We present a method that achieves state-of-the-art results for synthesizing novel views of
complex scenes by optimizing an underlying continuous volumetric scene function using a …

Recent advances in differentiable rendering have enabled high-quality reconstruction of 3D
scenes from multi-view images. Most methods rely on simple rendering algorithms: pre …