10 citations · 14 across the 8 of their papers we have counts for
8 papers
Guess The Unseen: Dynamic 3D Scene Reconstruction from Partial 2D Glimpses
Inhee Lee, Byungjun Kim, Hanbyul Joo
In this paper, we present a method to reconstruct the world and multiple dynamic humans in 3D from a monocular video input. As a key idea, we represent both the world and multiple…
PEGASUS: Personalized Generative 3D Avatars with Composable Attributes
Hyunsoo Cha, Byungjun Kim, Hanbyul Joo
We present PEGASUS, a method for constructing a personalized generative 3D face avatar from monocular video sources. Our generative 3D avatar enables disentangled controls to selec…
GALA: Generating Animatable Layered Assets from a Single Scan
Taeksoo Kim, Byungjun Kim, Shunsuke Saito +1
We present GALA, a framework that takes as input a single-layer clothed 3D human mesh and decomposes it into complete multi-layered 3D assets. The outputs can then be combined with…
Mocap Everyone Everywhere: Lightweight Motion Capture With Smartwatches and a Head-Mounted Camera
Jiye Lee, Hanbyul Joo
We present a lightweight and affordable motion capture method based on two smartwatches and a head-mounted camera. In contrast to the existing approaches that use six or more exper…
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…