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Estimating cloth simulation parameters from video
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Continuous capture of skin deformation
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SCAPE: shape completion and animation of people
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Capturing and animating occluded cloth
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Dynamic shape capture using multi-view photometric stereo
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Research problems in clothing simulation
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Data-Driven Estimation of Cloth Simulation Models
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Simultaneous shape and pose adaption of articulated models using linear optimization
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Refractive calibration of underwater cameras
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Image-based clothes animation for virtual fitting
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Content-aware exaggerated editing for life-like captured animations
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Interactive virtual try-on based on real-time motion capture
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Global Non-rigid Alignment of Surface Sequences
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Computers and Graphics
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We present a new performance capture approach that incorporates a physically-based cloth model to reconstruct a rigged fully-animatable virtual double of a real person in loose apparel from multi-view video recordings. Our algorithm only requires a minimum of manual interaction. Without the use of optical markers in the scene, our algorithm first reconstructs skeleton motion and detailed time-varying surface geometry of a real person from a reference video sequence. These captured reference performance data are then analyzed to automatically identify non-rigidly deforming pieces of apparel on the animated geometry. For each piece of apparel, parameters of a physically-based real-time cloth simulation model are estimated, and surface geometry of occluded body regions is approximated. The reconstructed character model comprises a skeleton-based representation for the actual body parts and a physically-based simulation model for the apparel. In contrast to previous performance capture methods, we can now also create new real-time animations of actors captured in general apparel.