Substantial progress has been made on modeling rigid 3D objects using deep implicit
representations. Yet, extending these methods to learn neural models of human shape is …

Existing 3D human pose estimators suffer poor generalization performance to new datasets,
largely due to the limited diversity of 2D-3D pose pairs in the training data. To address this …

Existing 3D scene understanding methods are heavily focused on 3D semantic and instance
segmentation. However, identifying objects and their parts only constitutes an intermediate …

Humans have long been recorded in a variety of forms since antiquity. For example,
sculptures and paintings were the primary media for depicting human beings before the …

Automatically producing instructions to modify one's posture could open the door to endless
applications, such as personalized coaching and in-home physical therapy. Tackling the …

We describe the first method to automatically estimate the 3D pose of the human body as
well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and …

We present a novel method for populating 3D indoor scenes with virtual humans that can
navigate in the environment and interact with objects in a realistic manner. Existing …

We present Octopus, a learning-based model to infer the personalized 3D shape of people
from a few frames (1-8) of a monocular video in which the person is moving with a …

We propose a viewpoint invariant model for 3D human pose estimation from a single depth
image. To achieve this, our discriminative model embeds local regions into a learned …

This paper presents WiPose, the first 3D human pose construction framework using
commercial WiFi devices. From the pervasive WiFi signals, WiPose can reconstruct 3D …