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 …

Recent work has demonstrated the significant potential of denoising diffusion models for
generating human motion, including text-to-motion capabilities. However, these methods are …

We address the problem of generating realistic 3D motions of humans interacting with
objects in a scene. Our key idea is to create a neural interaction field attached to a specific …

With the progress of 3D human pose and shape estimation, state-of-the-art methods can
either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases …

Animating virtual avatars with free-view control is crucial for various applications like virtual
reality and digital entertainment. Previous studies have attempted to utilize the …

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 …

Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose,
a versatile framework to model plausible 3D human poses for various applications. At the …

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 …

Abstract We present Recurrent Fitting (ReFit), a neural network architecture for single-image,
parametric 3D human reconstruction. ReFit learns a feedback-update loop that mirrors the …

Obtaining personalized 3D animatable avatars from a monocular camera has several real
world applications in gaming, virtual try-on, animation, and VR/XR, etc. However, it is very …