51 citations · 86 across the 15 of their papers we have counts for
4 papers · 2 filters
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation
Saurabh Saxena, Charles Herrmann, Junhwa Hur +4
Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity. We show that they also excel in estimating optical flow and…
A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence
Junyi Zhang, Charles Herrmann, Junhwa Hur +4
Text-to-image diffusion models have made significant advances in generating and editing high-quality images. As a result, numerous approaches have explored the ability of diffusion…
VQ3D: Learning a 3D-Aware Generative Model on ImageNet
Kyle Sargent, Jing Yu Koh, Han Zhang +5
Recent work has shown the possibility of training generative models of 3D content from 2D image collections on small datasets corresponding to a single object class, such as human…
Accidental Light Probes
Hong-Xing Yu, Samir Agarwala, Charles Herrmann +4
Recovering lighting in a scene from a single image is a fundamental problem in computer vision. While a mirror ball light probe can capture omnidirectional lighting, light probes a…