We view computer vision as the process of inferring the causes behind the images that we observe; that is, we want to infer the story behind the picture. The most interesting stories involve people. Consequently, our research focuses on understanding humans and their interactions with each other and with the 3D world.
Here, members of the Perceiving Systems Department write about their research.
Creating digital humans: Capture, Modeling, and Synthesis
ICCV 2021 papers in context
18 October 2021 Michael J. Black 16 minute read
Reflections on ICCV over 31 years
Computer vision from 1990 to 2021
14 October 2021 Michael J. Black 8 minute read
Could body shape determine the German election?
Our perception of leadership traits is influenced by body shape
Could biases in how we perceive body shapes influence the outcome of the German election? Using recent work from our group, we investigate which of the chancellor candidates is perceived to be a good leader, solely based on their body shape.
05 September 2021 Michael J. Black, Lea Müller 5 minute read
Modeling the 3D shape of animals
The SMAL animal model
We describe our work for modeling animal shape. This is joint work with Angjoo Kanazawa and Michael J. Black.
12 October 2020 Silvia Zuffi 5 minute read
The Secrets of the Secrets of Optical Flow
The following paper was awarded the Longuet-Higgins Prize at the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). The prize is given annually by the IEEE Pattern Analysis and Machine Intelligence (PAMI) Technical Committee for "Contributions in Computer ...
02 June 2020 Michael J. Black 6 minute read
The Road to Safe Self-driving
Optical flow prediction systems in autonomous cars.
A simple color patch could severely affect the optical flow prediction systems in autonomous cars
01 December 2019 Anurag Ranjan 5 minute read
The Perceiving Systems Department is a leading Computer Vision group in Germany.
We are part of the Max Planck Institute for Intelligent Systems in Tübingen — the heart of Cyber Valley.
We use Machine Learning to train computers to recover human behavior in fine detail, including face and hand movement. We also recover the 3D structure of the world, its motion, and the objects in it to understand how humans interact with 3D scenes.
By capturing human motion, and modeling behavior, we contibute realistic avatars to Computer Graphics.
To have an impact beyond academia we develop applications in medicine and psychology, spin off companies, and license technology. We make most of our code and data available to the research community.