We present an approach to reconstruct humans and track them over time. At the core of our
approach, we propose a fully" transformerized" version of a network for human mesh …

Existing motion capture datasets are largely short-range and cannot yet fit the need of long-
range applications. We propose LiDARHuman26M, a new human motion capture dataset …

We propose a Transformer-based framework for 3D human texture estimation from a single
image. The proposed Transformer is able to effectively exploit the global information of the …

We propose SMPLitex, a method for estimating and manipulating the complete 3D
appearance of humans captured from a single image. SMPLitex builds upon the recently …

Although novel 3D animation techniques could be boosted by a large variety of deep
learning methods, flexible automatic 3D applications (involving animated figures such as …

While 3D body reconstruction methods have made remarkable progress recently, it remains
difficult to acquire the sufficiently accurate and numerous 3D supervisions required for …

We present a novel approach for tracking multiple people in video. Unlike past approaches
which employ 2D representations, we focus on using 3D representations of people, located …

Recent progress in human shape learning shows that neural implicit models are effective in
generating 3D human surfaces from limited number of views and even from a single RGB …

Modern software development paradigms, including Service-Oriented Architecture (SOA),
tend to make use of available services eg, web service Application Programming Interfaces …

We propose a novel framework to reconstruct super-resolution human shape from a single
low-resolution input image. The approach overcomes limitations of existing approaches that …