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 …

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 …

We propose a method to reconstruct global human trajectories from videos in the wild. Our
optimization method decouples the camera and human motion, which allows us to place …

Although the estimation of 3D human pose and shape (HPS) is rapidly progressing, current
methods still cannot reliably estimate moving humans in global coordinates, which is critical …

Capturing the interactions between humans and their environment in 3D is important for
many applications in robotics, graphics, and vision. Recent works to reconstruct the 3D …

In this work we study the benefits of using tracking and 3D poses for action recognition. To
achieve this, we take the Lagrangian view on analysing actions over a trajectory of human …

We present Multi-HMR, a strong single-shot model for multi-person 3D human mesh
recovery from a single RGB image. Predictions encompass the whole body, ie., including …

The field of multi-object tracking has recently seen a renewed interest in the good old
schema of tracking-by-detection, as its simplicity and strong priors spare it from the complex …

Existing volumetric methods for predicting 3D human pose estimation are accurate, but
computationally expensive and optimized for single time-step prediction. We present …

Recent advancements in language models have demonstrated their adeptness in
conducting multi-turn dialogues and retaining conversational context. However, this …