Existing self-supervised 3D human pose estimation schemes have largely relied on weak
supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior …

We introduce a new dataset, Human3. 6M, of 3.6 Million accurate 3D Human poses,
acquired by recording the performance of 5 female and 6 male subjects, under 4 different …

This paper demonstrates accurate human pose estimation through walls and occlusions. We
leverage the fact that wireless signals in the WiFi frequencies traverse walls and reflect off …

The availability of the large-scale labeled 3D poses in the Human3. 6M dataset plays an
important role in advancing the algorithms for 3D human pose estimation from a still image …

The goal of this paper is to take a single 2D image of a scene and recover the 3D structure in
terms of a small set of factors: a layout representing the enclosing surfaces as well as a set …

We present imGHUM, the first holistic generative model of 3D human shape and articulated
pose, represented as a signed distance function. In contrast to prior work, we model the full …

Top-down methods dominate the field of 3D human pose and shape estimation, because
they are decoupled from human detection and allow researchers to focus on the core …

We propose Hi4D, a method and dataset for the auto analysis of physically close human-
human interaction under prolonged contact. Robustly disentangling several in-contact …

Our ability to train end-to-end systems for 3D human pose estimation from single images is
currently constrained by the limited availability of 3D annotations for natural images. Most …

Estimating 3D human poses from monocular videos is a challenging task due to depth
ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting …