HIGH QUALITY HUMAN 3D BODY MODELING, TRACKING AND APPLICATION

Geometric reconstruction of dynamic objects is a fundamental task of computer vision and graphics, and modeling human body of high fidelity is considered to be a core of this problem. Traditional human shape and motion capture techniques require an array of surrounding cameras or subjects wear reflective markers, resulting in a limitation of working space and portability. In this dissertation, a complete process is designed from geometric modeling detailed 3D human full body and capturing shape dynamics over time using a flexible setup to guiding clothes/person re-targeting with such data-driven models. As the mechanical movement of human body can be considered as an articulate motion, which is easy to guide the skin animation but has difficulties in the reverse process to find parameters from images without manual intervention, we present a novel parametric model, GMM-BlendSCAPE, jointly taking both linear skinning model and the prior art of BlendSCAPE (Blend Shape Completion and Animation for PEople) into consideration and develop a Gaussian Mixture Model (GMM) to infer both body shape and pose from incomplete observations. We show the increased accuracy of joints and skin surface estimation using our model compared to the skeleton based motion tracking. To model the detailed body, we start with capturing high-quality partial 3D scans by using a single-view commercial depth camera. Based on GMM-BlendSCAPE, we can then reconstruct multiple complete static models of large pose difference via our novel non-rigid registration algorithm. With vertex correspondences established, these models can be further converted into a personalized drivable template and used for robust pose tracking in a similar GMM framework. Moreover, we design a general purpose real-time non-rigid deformation algorithm to accelerate this registration. Last but not least, we demonstrate a novel virtual clothes try-on application based on our personalized model utilizing both image and depth cues to synthesize and re-target clothes for single-view videos of different people

Browsing Large Image Datasets through Voronoi Diagrams

Conventional browsing of image collections use mechanisms such as thumbnails arranged on a regular grid or on a line, often mounted over a scrollable panel. However, this approach does not scale well with the size of the datasets (number of images). In this paper, we propose a new thumbnail-based interface to browse large collections of images. Our approach is based on weighted centroidal anisotropic Voronoi diagrams. A dynamically changing subset of images is represented by thumbnails and shown on the screen. Thumbnails are shaped like general polygons, to better cover screen space, while still reflecting the original aspect ratios or orientation of the represented images. During the browsing process, thumbnails are dynamically rearranged, reshaped and rescaled. The objective is to devote more screen space (more numerous and larger thumbnails) to the parts of the dataset closer to the current region of interest, and progressively lesser away from it, while still making the dataset visible as a whole. During the entire process, temporal coherence is always maintained. GPU implementation easily guarantees the frame rates needed for fully smooth interactivity

The effects of body exposure on self-body image and esthetic appreciation in anorexia nervosa.

Repeated exposures to thin-idealized body shapes may alter women's perceptions of what normal (e.g., accepted) and ideal (e.g., desired) bodies in a cultural environment look like. The aim of the present study was to investigate whether exposure to thin and round body shapes may change the subsequent esthetic appreciation of others' bodies and the perceptual and cognitive-affective dimensions of self-body image in patients suffering from anorexia nervosa (AN). Thirteen AN patients and 13 matched healthy controls were exposed to pictures of either thin or round unfamiliar body models and, before and after exposure, they were required to either express liking judgments about round and slim figures of unfamiliar bodies (esthetic task) or to adjust distorted pictures of their own body to their perceptual (How do you see yourself?), affective (How do you feel yourself?), metacognitive (How do others see you?) and ideal (How would you like to look like?) body image (self-body adjustment task). Brief exposures to round models increased liking judgments of round figures in both groups. However, only in AN patients, exposure to round models induced an increase in thin figures liking, which positively correlated with their preoccupation with dieting. Furthermore, exposure to round bodies in AN patients, but not in controls, increased the distortion for the perceptual body image and decreased the size of the ideal one. No differences between the two groups were obtained after adaptation to thin models. Our results suggest that AN patients' perception of their own and others' body is more easily malleable by exposure to round figures as compared to controls. Crucially, this mechanism may strongly contribute to the development and maintenance of self-body image disturbances