Human Pose, Shape and Action
3D Pose from Images
2D Pose from Images
Beyond Motion Capture
Action and Behavior
Body Perception
Body Applications
Pose and Motion Priors
Clothing Models (2011-2015)
Reflectance Filtering
Learning on Manifolds
Markerless Animal Motion Capture
Multi-Camera Capture
2D Pose from Optical Flow
Body Perception
Neural Prosthetics and Decoding
Part-based Body Models
Intrinsic Depth
Lie Bodies
Layers, Time and Segmentation
Understanding Action Recognition (JHMDB)
Intrinsic Video
Intrinsic Images
Action Recognition with Tracking
Neural Control of Grasping
Flowing Puppets
Faces
Deformable Structures
Model-based Anthropometry
Modeling 3D Human Breathing
Optical flow in the LGN
FlowCap
Smooth Loops from Unconstrained Video
PCA Flow
Efficient and Scalable Inference
Motion Blur in Layers
Facade Segmentation
Smooth Metric Learning
Robust PCA
3D Recognition
Object Detection
Body Perception

Body representation is an essential part of a person’s self-concept and also shapes how we see the world. A disturbed body representation also plays a role in clinical conditions such as eating disorders or stroke. So far, a major hurdle for research was the lack of ecologically valid body stimuli. Psychological and medical research so far relies on indirect experimental tasks (e.g. estimated reach) or on questionnaires that assess explicit body attitudes. In this project, we cooperate with partners from the Max-Planck-Institute for Biological Cybernetics and the University Hospital Tübingen to develop ecologically valid methods for the assessment of body representation.
As a next level of existing experimental setups, we created a virtual reality mirror scenario []. Based on a 3D body scan and a statistical body model learned from the CAESAR dataset, we generate individual avatars of the participants that can be distorted in terms of weight. Through texture manipulations, we are able to vary the identity of the displayed person. As a major improvement to the existing artist-generated figural drawing scales, we also created a biometric figure rating scale [
] and a desktop tool. In different projects, we assessed >100 participants from the general population as well as >30 women with anorexia nervosa.
Our results in [] show that in the general population, the accuracy of own body size estimation is predicted by personal BMI, such that participants with lower BMI underestimated their body size and participants with higher BMI overestimated their body size. Critically, these biases suggest that people tend to perceive their weight in an exaggerated way, while there was no hint of a general denial in underweight or overweight persons. The same underestimation bias also occurred in women with anorexia nervosa. Further, we consistently observed that women with anorexia nervosa favored a much thinner body as ideal weight than healthy women. This observation has major clinical implications, because it questions the common idea that misperception of body dimensions may be a maintaining mechanism of this eating disorder. Rather, it suggests that treatment should support patients in accepting a healthy body weight for their own.
In ongoing work, we are making tools for avatar creation and VR experimentation much simpler so that other researchers can readily use our body models and methods.
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