3D body reconstruction from photos based on range scan

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We present a data-driven shape model for reconstructing human body models from one or more 2D photos. One of the key tasks in reconstructing the 3D model from image data is shape recovery, a task done until now in utterly geometric way, in the domain of human body modeling. In contrast, we adopt a data-driven, parameterized deformable model that is acquired from a collection of range scans of real human body. The key idea is to complement the image-based reconstruction method by leveraging the quality shape and statistic information accumulated from multiple shapes of range-scanned people. In the presence of ambiguity either from the noise or missing views, our technique has a bias towards representing as much as possible the previously acquired 'knowledge' on the shape geometry. Texture coordinates are then generated by projecting the modified deformable model onto the front and back images. Our technique has shown to reconstruct successfully human body models from minimum number images, even from a single image input.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2006
Language
English
Article Type
Article; Proceedings Paper
Citation

TECHNOLOGIES FOR E-LEARNING AND DIGITAL ENTERTAINMENT, PROCEEDINGS BOOK SERIES: LECTURE NOTES IN COMPUTER SCIENCE, v.3942, pp.849 - 860

ISSN
0302-9743
URI
http://hdl.handle.net/10203/90859
Appears in Collection
GCT-Journal Papers(저널논문)
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