55 citations · 195 across the 24 of their papers we have counts for
6 papers · 1 filter
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
Benjamin Biggs, Sébastien Ehrhadt, Hanbyul Joo +3
We consider the problem of obtaining dense 3D reconstructions of humans from single and partially occluded views. In such cases, the visual evidence is usually insufficient to iden…
FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration
Yu Rong, Takaaki Shiratori, Hanbyul Joo
Although the essential nuance of human motion is often conveyed as a combination of body movements and hand gestures, the existing monocular motion capture approaches mostly focus…
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
Jason Y. Zhang, Sam Pepose, Hanbyul Joo +3
We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontro…
Body2Hands: Learning to Infer 3D Hands from Conversational Gesture Body Dynamics
Evonne Ng, Shiry Ginosar, Trevor Darrell +1
We propose a novel learned deep prior of body motion for 3D hand shape synthesis and estimation in the domain of conversational gestures. Our model builds upon the insight that bod…
Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation
Hanbyul Joo, Natalia Neverova, Andrea Vedaldi
Differently from 2D image datasets such as COCO, large-scale human datasets with 3D ground-truth annotations are very difficult to obtain in the wild. In this paper, we address thi…
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
Shunsuke Saito, Tomas Simon, Jason Saragih +1
Recent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. Although current…