55 citations · 105 across the 11 of their papers we have counts for
18 papers · 1 filter
CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images
Sookwan Han, Hanbyul Joo
We present a method for teaching machines to understand and model the underlying spatial common sense of diverse human-object interactions in 3D in a self-supervised way. This is a…
NCHO: Unsupervised Learning for Neural 3D Composition of Humans and Objects
Taeksoo Kim, Shunsuke Saito, Hanbyul Joo
Deep generative models have been recently extended to synthesizing 3D digital humans. However, previous approaches treat clothed humans as a single chunk of geometry without consid…
Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic Models
Byungjun Kim, Patrick Kwon, Kwangho Lee +4
We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic detai…
Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion
Evonne Ng, Hanbyul Joo, Liwen Hu +4
We present a framework for modeling interactional communication in dyadic conversations: given multimodal inputs of a speaker, we autoregressively output multiple possibilities of…
D3D-HOI: Dynamic 3D Human-Object Interactions from Videos
Xiang Xu, Hanbyul Joo, Greg Mori +1
We introduce D3D-HOI: a dataset of monocular videos with ground truth annotations of 3D object pose, shape and part motion during human-object interactions. Our dataset consists of…
FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration
Yu Rong, Takaaki Shiratori, Hanbyul Joo
Most existing monocular 3D pose estimation approaches only focus on a single body part, neglecting the fact that the essential nuance of human motion is conveyed through a concert…