Estimating the pose of a human in 3D given an image or a video has recently received
significant attention from the scientific community. The main reasons for this trend are the …

We address the problem of generating diverse 3D human motions from textual descriptions.
This challenging task requires joint modeling of both modalities: understanding and …

Human pose estimation refers to the estimation of the location of body parts and how they
are connected in an image. Human pose estimation from monocular images has wide …

Recent transformer-based solutions have been introduced to estimate 3D human pose from
2D keypoint sequence by considering body joints among all frames globally to learn spatio …

We tackle the problem of action-conditioned generation of realistic and diverse human
motion sequences. In contrast to methods that complete, or extend, motion sequences, this …

Data imbalance exists ubiquitously in real-world visual regressions, eg, age estimation and
pose estimation, hurting the model's generalizability and fairness. Thus, imbalanced …

In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully
convolutional model based on dilated temporal convolutions over 2D keypoints. We also …

Recent transformer-based solutions have shown great success in 3D human pose
estimation. Nevertheless, to calculate the joint-to-joint affinity matrix, the computational cost …

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for
regression. Current architectures of GCNs are limited to the small receptive field of …

A key requirement for leveraging supervised deep learning methods is the availability of
large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very …