Realistic virtual humans for VR therapy of body image disorders
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Datum
2025
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Zusammenfassung
Stephan Wenninger - Realistic Virtual Humans for VR Therapy of Body Image Disorders
Abstract:
This thesis presents methods for reconstructing and modifying realistic personalized virtual humans to be employed in the context of a VR-based body image disorder therapy system.
We start by presenting a method for generating virtual humans from monocular smartphone cameras, thereby lowering the hardware requirements and increasing the availability of personalized virtual humans compared to other methods, which typically depend on elaborate photogrammetry rigs.
In a user study, we investigate the perception of the resulting virtual humans by scanning people with both the low-cost smartphone-based method and a standard multi-view stereo photogrammetry rig.
Participants then embody and rate both virtual humans in a virtual mirror exposure scenario.
The results show, that both virtual humans are perceived similarly, indicating that our smartphone-based method presents a viable alternative to expensive photogrammetry rigs.
For employing realistic virtual humans in body image therapy, we present a method for modifiying the body weight of the virtual humans in real-time.
Users of the VR-based therapy system then embody a personalized avatar in a virtual mirror exposure scenario and are given active control over the avatar's body shape, enabling researchers to investigate the potential of VR-based therapy and gain insight into possibly occurring body image disorders.
To improve on the purely surface-based body weight modification model, the second part of this thesis focuses on anatomical representations of virtual humans.
We present a method for inferring anatomical details from a given skin surface in less than a minute.
To this end, we derive a three-layered anatomical model, consisting of a skin, muscle, and skeleton layer, from a commercial high-resolution anatomical model.
We then learn a model for predicting body composition, i.e., fat and muscle mass, from a given skin surface and fit the template model to a large database of surface scans while conforming to the estimated body composition.
The original high-resolution anatomical structures are transferred to the resulting fit via a triharmonic space warp.
Finally, we use the inferred anatomical data to learn an anatomically constrained volumetric human shape model.
We enlarge our training data to the full Cartesian product of all skeleton shapes and all soft tissue distributions using physically plausible volumetric deformation transfer.
A self-supervised learning technique then produces two separate latent parameter sets, allowing us to sample different soft tissue distributions over the same skeleton shape and vice versa.
The resulting anatomical model additionally facilitates fast skeleton inference and semantic localized shape modification.
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Inhaltsverzeichnis
Schlagwörter
Virtual humans, Geometry processing, Statistical human body models
Schlagwörter nach RSWK
Avatar (Informatik), Geometrieverarbeitung