Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Human pose estimation aims to locate the human body parts and build human body
representation (eg, body skeleton) from input data such as images and videos. It has drawn …

We study a challenging task, conditional human motion generation, which produces
plausible human motion sequences according to various conditional inputs, such as action …

Being data-driven is one of the most iconic properties of deep learning algorithms. The birth
of ImageNet drives a remarkable trend of" learning from large-scale data" in computer vision …

3D avatar creation plays a crucial role in the digital age. However, the whole production
process is prohibitively time-consuming and labor-intensive. To democratize this technology …

We show, for the first time, that neural networks trained only on synthetic data achieve state-
of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real …

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 …

Despite significant progress, we show that state of the art 3D human pose and shape
estimation methods remain sensitive to partial occlusion and can produce dramatically …

Whole-body mesh recovery aims to estimate the 3D human body, face, and hands
parameters from a single image. It is challenging to perform this task with a single network …

Estimating 3D humans from images often produces implausible bodies that lean, float, or
penetrate the floor. Such methods ignore the fact that bodies are typically supported by the …