Capture

We propose a novel calibration method based on the usage of directionally encoded light rays for estimating the intrinsic parameters.

We present a real-time multi-view facial capture system facilitated by synthetic training imagery.

We present the first method for capturing dynamic hair and automatically determining the physical properties for simulating the observed hairstyle in motion.

We propose a framework for motion capture using sparse multi-modal sensor sets, including data obtained from optical markers and inertial measurement units.

We quantitatively and qualitatively show that our monocular approach reconstructs higher quality lip shapes, even for complex shapes like a kiss or lip rolling than previous monocular approaches.

In this paper, we, therefore, present the first approach for non-invasive reconstruction of an entire person-specific tooth row from just a sparse set of photographs of the mouth region.

Synthetic training significantly reduces the capture and annotation burden and in theory allows generation of an arbitrary amount of data.

We present the first approach for high-quality lightweight eye capture, which leverages a database of pre-captured eyes to guide the reconstruction of new eyes from much less constrained inputs, such as traditional single-shot face scanners or even a single photo from the internet.

We propose a local deformation model composed of many small subspaces spatially distributed over the face.

This paper proposes photogeometric scene flow (PGSF) for high-quality dynamic 3D reconstruction.