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20212023
most citedSemantic keypoint-based pose estimation from single RGB frames

8 citations · 26 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV20231 cited

GART: Gaussian Articulated Template Models

Jiahui Lei, Yufu Wang, Georgios Pavlakos +2

We introduce Gaussian Articulated Template Model GART, an explicit, efficient, and expressive representation for non-rigid articulated subject capturing and rendering from monocula…

cs.CV20221 cited

Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary

Shiting Xiao, Yufu Wang, Ammon Perkes +4

The ability to capture detailed interactions among individuals in a social group is foundational to our study of animal behavior and neuroscience. Recent advances in deep learning…

cs.CV20228 cited

Semantic keypoint-based pose estimation from single RGB frames

Karl Schmeckpeper, Philip R. Osteen, Yufu Wang +6

This paper presents an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional…

cs.CV2022

Cross-modal Map Learning for Vision and Language Navigation

Georgios Georgakis, Karl Schmeckpeper, Karan Wanchoo +4

We consider the problem of Vision-and-Language Navigation (VLN). The majority of current methods for VLN are trained end-to-end using either unstructured memory such as LSTM, or us…

cs.RO20222 cited

Uncertainty-driven Planner for Exploration and Navigation

Georgios Georgakis, Bernadette Bucher, Anton Arapin +3

We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constit…

cs.LG20217 cited

Discovering and Achieving Goals via World Models

Russell Mendonca, Oleh Rybkin, Kostas Daniilidis +2

How can artificial agents learn to solve many diverse tasks in complex visual environments in the absence of any supervision? We decompose this question into two problems: discover…