119 citations · 310 across the 17 of their papers we have counts for
8 papers · 1 filter
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…
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
Nataniel Ruiz, Yuanzhen Li, Varun Jampani +6
Personalization has emerged as a prominent aspect within the field of generative AI, enabling the synthesis of individuals in diverse contexts and styles, while retaining high-fide…
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…
ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections
Chun-Han Yao, Amit Raj, Wei-Chih Hung +4
Estimating 3D articulated shapes like animal bodies from monocular images is inherently challenging due to the ambiguities of camera viewpoint, pose, texture, lighting, etc. We pro…
StyleDrop: Text-to-Image Generation in Any Style
Kihyuk Sohn, Nataniel Ruiz, Kimin Lee +11
Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distributi…
Score-Based Diffusion Models as Principled Priors for Inverse Imaging
Berthy T. Feng, Jamie Smith, Michael Rubinstein +3
Priors are essential for reconstructing images from noisy and/or incomplete measurements. The choice of the prior determines both the quality and uncertainty of recovered images. W…