Marker-Free Human Performance Capture with Synthetic Data: Towards Holistic Face, Body, and Hand Reconstruction

Invited talk by Shideh Rezaifar (Microsoft Mixed Reality & AI lab Zurich)

This talk presents a new approach to marker-free, holistic human performance capture that eliminates the need for complex hardware, markers, or manual calibration. By leveraging machine learning models trained exclusively on synthetic data, the method enables high-fidelity reconstruction of the entire human body, including face, hands, eyes, and tongue in a wide range of environments and camera setups. The approach combines synthetic training data with parametric human models to produce stable, accurate, and world-space results without manual intervention. The talk will explore how synthetic data addresses annotation challenges, enhances generalization, and paves the way for more accessible and flexible human motion capture.

Veranstaltung übernehmen als iCal