most citedReconstructing Articulated Rigged Models from RGB-D Videos

3 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20233 cited

DECO: Dense Estimation of 3D Human-Scene Contact In The Wild

Shashank Tripathi, Agniv Chatterjee, Jean-Claude Passy +3

Understanding how humans use physical contact to interact with the world is key to enabling human-centric artificial intelligence. While inferring 3D contact is crucial for modelin…

cs.CV2023

POCO: 3D Pose and Shape Estimation with Confidence

Sai Kumar Dwivedi, Cordelia Schmid, Hongwei Yi +2

The regression of 3D Human Pose and Shape (HPS) from an image is becoming increasingly accurate. This makes the results useful for downstream tasks like human action recognition or…

cs.CV20231 cited

Reconstructing Signing Avatars From Video Using Linguistic Priors

Maria-Paola Forte, Peter Kulits, Chun-Hao Huang +4

Sign language (SL) is the primary method of communication for the 70 million Deaf people around the world. Video dictionaries of isolated signs are a core SL learning tool. Replaci…

cs.CV2023

Detecting Human-Object Contact in Images

Yixin Chen, Sai Kumar Dwivedi, Michael J. Black +1

Humans constantly contact objects to move and perform tasks. Thus, detecting human-object contact is important for building human-centered artificial intelligence. However, there e…

cs.CV20163 cited

Reconstructing Articulated Rigged Models from RGB-D Videos

Dimitrios Tzionas, Juergen Gall

Although commercial and open-source software exist to reconstruct a static object from a sequence recorded with an RGB-D sensor, there is a lack of tools that build rigged models o…