activity
20182022
most citedImproved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation

27 citations · 45 across the 9 of their papers we have counts for

collaborators

20 papers

cs.CV20222 cited

Fast-SNARF: A Fast Deformer for Articulated Neural Fields

Xu Chen, Tianjian Jiang, Jie Song +4

Neural fields have revolutionized the area of 3D reconstruction and novel view synthesis of rigid scenes. A key challenge in making such methods applicable to articulated objects,…

cs.HC20228 cited

Computational Design of Active Kinesthetic Garments

Velko Vechev, Ronan Hinchet, Stelian Coros +2

Garments with the ability to provide kinesthetic force-feedback on-demand can augment human capabilities in a non-obtrusive way, enabling numerous applications in VR haptics, motio…

cs.CV2022

Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors

Xi Wang, Gen Li, Yen-Ling Kuo +3

We present a method for inferring diverse 3D models of human-object interactions from images. Reasoning about how humans interact with objects in complex scenes from a single 2D im…

cs.GR2022

Computational Design of Kinesthetic Garments

Velko Vechev, Juan Zarate, Bernhard Thomaszewski +1

Kinesthetic garments provide physical feedback on body posture and motion through tailored distributions of reinforced material. Their ability to selectively stiffen a garment's re…

cs.CV2022

PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence

Zijian Dong, Chen Guo, Jie Song +3

We present a novel method to learn Personalized Implicit Neural Avatars (PINA) from a short RGB-D sequence. This allows non-expert users to create a detailed and personalized virtu…

cs.CV2022

LiP-Flow: Learning Inference-time Priors for Codec Avatars via Normalizing Flows in Latent Space

Emre Aksan, Shugao Ma, Akin Caliskan +5

Neural face avatars that are trained from multi-view data captured in camera domes can produce photo-realistic 3D reconstructions. However, at inference time, they must be driven b…