Our research is at the intersection between computer vision, computer graphics and machine learning--we develop computational algorithms to efficiently digitize people and train machines to perceive people from visual data.

Current computer vision algorithms can detect people in images or estimate 2D keypoints to a remarkable accuracy. However, people are far more complex–-we effortlessly sense other people's emotional state based on facial expressions and body movements, or we make guesses about people's preferences based on what clothing they wear. Our goal is to build virtual humans that look, move and eventually think like real ones.

News

3 papers accepted at 3DV 2018!
1 Paper won the best student paper award!
September 2018

Pdfs and videos available!

-Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation
3DV Best Student Paper Award

-Detailed Human Avatars from Monocular Video

-Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB

1 paper at ECCV'18
September 2018

Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera

We will release the first (and most challenging) dataset of natural scenes with multiple people with accurate 3D pose and shape! The RGB video includes scenes like taking the bus, walking on the city, shoping, sports, etc. Stay tuned!

PeopleCap'18 workshop at ECCV'18
September 14th 2018

Gerard Pons-Moll and Jonathan Taylor will organize the second edition of PeopleCap.

The workshop will bring together researchers in the fields of 3D human modelling, reconstruction and tracking. Submission deadline is July 20th

Science article about our CVPR'18 paper
April 13th 2018

One of our CVPR papers has been covered in the Science magazine.

We developed a method to create a 3D avatar from a few seconds of video footage. See the Paper.

2 papers at CVPR. One oral and one spotlight
March 1st 2018

Video Based Reconstruction of 3D People Models

DoubleFusion: Real-time Capture of Human Performance with Inner Body Shape from a Depth Sensor

Best Paper Award at Eurographics 2017
April 2017

Our paper on human pose estimation from sparse inertial sensors won the Eurographics'17 best paper award!

Latest Publications

Deep Inertial Poser Learning to Reconstruct Human Pose from SparseInertial Measurements in Real Time
Yinghao Huang, Manuel Kaufmann, Emre Aksan, Michael J. Black, Otmar Hilliges, Gerard Pons-Moll
Deep Inertial Poser Learning to Reconstruct Human Pose from SparseInertial Measurements in Real Time
in ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), vol. 37, no. 6, 185:1-185:15 2018.
Detailed Human Avatars from Monocular Video
Thiemo Alldieck, Marcus Magnor, Weipeng Xu, Christian Theobalt, Gerard Pons-Moll
Detailed Human Avatars from Monocular Video
in International Conference on 3D Vision (3DV), 2018.
Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation
Mohamed Omran, Christoph Lassner, Gerard Pons-Moll, Peter Gehler, Bernt Schiele
Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation
in International Conference on 3D Vision (3DV), 2018.
Oral, 3DV Best Student Paper Award
All publications