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We combine research on computer vision, computer graphics, and machine learning to teach computers to see and understand humans and their behavior. By capturing 3D human behavior from video at scale, we train digital humans that behave like real humans.

Our research uses Computer Vision to learn digital humans that can perceive, learn, and act in virtual 3D worlds. This involves capturing the shape, appearance, and motion of real people as well as their interactions with each other and the 3D scene using monocular video. We leverage this to learn generative models of people and their behavior and evaluate these models by synthesizing realistic looking humans behaving in virtual worlds.

This work combines Computer Vision, Machine Learning, and Computer Graphics.

Director
Michael Black

Michael Black

Emeritus / Acting Director
Admin Team
Melanie Feldhofer

Melanie Feldhofer

Department Manager
Nicole Overbaugh

Nicole Overbaugh

Office Coordinator
+49 7071 601 1800

Perceiving Systems Highlights

Talk Perceiving Systems 2026-02-17

SAM3D Body

SAM3D Body
Article Haptic Intelligence Perceiving Systems 2025-04-01

Wrist-to-Wrist Bioimpedance Can Reliably Detect Discrete Self-Touch

Wrist-to-Wrist Bioimpedance Can Reliably Detect Discrete Self-Touch IEEE Transactions on Instrumentation and Measurement
Article Perceiving Systems 2024-12-01

PuzzleAvatar: Assembling 3D Avatars from Personal Albums

{PuzzleAvatar}: Assembling {3D} Avatars from Personal Albums ACM Transactions on Graphics