119 citations · 310 across the 17 of their papers we have counts for
6 papers · 1 filter
Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery from Sparse Image Ensemble
Chun-Han Yao, Wei-Chih Hung, Yuanzhen Li +3
Automatically estimating 3D skeleton, shape, camera viewpoints, and part articulation from sparse in-the-wild image ensembles is a severely under-constrained and challenging proble…
SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow
Itai Lang, Dror Aiger, Forrester Cole +2
Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations. Recently, there have been…
TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency
Medhini Narasimhan, Arsha Nagrani, Chen Sun +4
YouTube users looking for instructions for a specific task may spend a long time browsing content trying to find the right video that matches their needs. Creating a visual summary…
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani +3
Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. However, these models…
LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery
Chun-Han Yao, Wei-Chih Hung, Yuanzhen Li +3
Creating high-quality articulated 3D models of animals is challenging either via manual creation or using 3D scanning tools. Therefore, techniques to reconstruct articulated 3D obj…
Disentangling Architecture and Training for Optical Flow
Deqing Sun, Charles Herrmann, Fitsum Reda +3
How important are training details and datasets to recent optical flow models like RAFT? And do they generalize? To explore these questions, rather than develop a new model, we rev…