4.3k citations · 10.6k across the 23 of their papers we have counts for
8 papers · 1 filter
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
Stephanie Fu, Mark Hamilton, Laura Brandt +3
Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime. Ho…
3D Motion Magnification: Visualizing Subtle Motions with Time Varying Radiance Fields
Brandon Y. Feng, Hadi Alzayer, Michael Rubinstein +2
Motion magnification helps us visualize subtle, imperceptible motion. However, prior methods only work for 2D videos captured with a fixed camera. We present a 3D motion magnificat…
Background Prompting for Improved Object Depth
Manel Baradad, Yuanzhen Li, Forrester Cole +4
Estimating the depth of objects from a single image is a valuable task for many vision, robotics, and graphics applications. However, current methods often fail to produce accurate…
Materialistic: Selecting Similar Materials in Images
Prafull Sharma, Julien Philip, Michaël Gharbi +3
Separating an image into meaningful underlying components is a crucial first step for both editing and understanding images. We present a method capable of selecting the regions of…
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention
Guangxuan Xiao, Tianwei Yin, William T. Freeman +2
Diffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-sp…
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue +2
We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space…