Learning Digital Humans in Motion

Introduction

We are taking part in a revolution of how people perceive each other and how they communicate in the digital space. Emerging technologies demonstrate how humans can be digitized such that their digital doubles (digital humans) can be viewed in mixed or virtual reality applications.

Importance of 3D Digital Humans

Especially for immersive telepresence, these 3D digital humans are of high interest since people can see each other in augmented (AR) or virtual reality (VR) and can interact with each other. For an immersive experience, this requires:

• A high-quality level of appearance reproduction
• The estimation of subtle motions of the human

Hardware Considerations

Besides quality aspects, digital humans need to be reconstructed and driven by consumer-grade hardware solutions (e.g., a webcam) to enable a wide applicability of digital humans for:

1. Immersive telecommunication
2. Virtual mirrors (e-commerce)
3. Other entertainment purposes (e.g., computer games)

Research Focus

In this proposal, we concentrate on the computer vision and graphics aspects of capturing and rendering (realizing) motions and appearances of photo-realistic digital humans reconstructed with commodity hardware and ask the question:

Can we leverage natural language for the reconstruction, representation, and modelling of the appearance, motion, and interactions of digital humans?

Motion and Interaction Analysis

As the face and gestures play an important role during conversations, we focus our research on the upper body of the human, aiming at capturing and synthesizing complete, contact-rich digital humans with life-like motions.

We will analyze how humans move and will develop data-driven motion synthesis methods that learn how to generate talking and listening motion with interactions of the hand with the face.