Marker-based motion capture (mocap) is widely criticized as producing lifeless animations.
We argue that important information about body surface motion is present in standard …

To look human, digital full-body avatars need to have soft-tissue deformations like those of
real people. We learn a model of soft-tissue deformations from examples using a high …

Statistical models of 3D human shape and pose learned from scan databases have
developed into valuable tools to solve a variety of vision and graphics problems …

We describe how to learn a compact and efficient model of the surface deformation of
human hands. The model is built from a set of noisy depth images of a diverse set of …

We propose a method that extracts sparse and spatially localized deformation modes from
an animated mesh sequence. To this end, we propose a new way to extend the theory of …

We present SoftSMPL, a learning‐based method to model realistic soft‐tissue dynamics as a
function of body shape and motion. Datasets to learn such task are scarce and expensive to …

We develop a novel approach to generate human body models in a variety of shapes and
poses via tuning semantic parameters. Our approach is investigated with datasets of up to …

We propose a method to create a wide range of human body shapes from a single input 3D
anatomy template. Our approach is inspired by biological processes responsible for human …

We propose a hardware and software pipeline to fabricate flexible wearable sensors and
use them to capture deformations without line-of-sight. Our first contribution is a low-cost …

A method of registering images at subpixel accuracy has been proposed, which does not
resort to interpolation. The method is based on the phase correlation method and is …