Human parsing aims to partition humans in image or video into multiple pixel-level semantic
parts. In the last decade, it has gained significantly increased interest in the computer vision …

In this paper we introduce SMPLicit, a novel generative model to jointly represent body
pose, shape and clothing geometry. In contrast to existing learning-based approaches that …

While recent work has shown progress on extracting clothed 3D human avatars from a
single image, video, or a set of 3D scans, several limitations remain. Most methods use a …

To address the challenging task of instance-aware human part parsing, a new bottom-up
regime is proposed to learn category-level human semantic segmentation as well as multi …

In this work, we study the challenging problem of instance-aware human body part parsing.
We introduce a new bottom-up regime which achieves the task through learning category …

We present animatable neural radiance fields (animatable NeRF) for detailed human avatar
creation from monocular videos. Our approach extends neural radiance fields (NeRF) to the …

Abstract Panoptic Part Segmentation (PPS) aims to unify panoptic segmentation and part
segmentation into one task. Previous work mainly utilizes separated approaches to handle …

We propose a novel optimization-based paradigm for 3D human model fitting on images
and scans. In contrast to existing approaches that directly regress the parameters of a low …

Segmenting humans in 3D indoor scenes has become increasingly important with the rise of
human-centered robotics and AR/VR applications. To this end, we propose the task of joint …

Recent approaches to drape garments quickly over arbitrary human bodies leverage self-
supervision to eliminate the need for large training sets. However, they are designed to train …