6/4/2023 0 Comments Meshlab filling in holes![]() These results show the feasibility of the proposed matching scheme.read more read lessĪbstract: We present a novel approach for obtaining a complete and consistent 3D model representation from incomplete surface scans, using a database of 3D shapes to provide geometric priors for regions of missing data. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. The weighted sum rule is applied to combine the scores given by the two matching components. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. The surface matching component is based on a modified iterative closest point (ICP) algorithm. The recognition engine consists of two components, surface matching and appearance-based matching. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. ![]() 2.5D is a simplified 3D (x,y,z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. In particular, our method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person.read more read lessĪbstract: The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We present applications of shape completion to partial view completion and motion capture animation. We show how the model can be used for shape completion - generating a complete surface mesh given a limited set of markers specifying the target shape. Our two models can be combined to produce 3D surface models with realistic muscle deformation for different people in different poses, when neither appear in the training set. ![]() We also learn a separate model of variation based on body shape. We learn a pose deformation model that derives the non-rigid surface deformation as a function of the pose of the articulated skeleton. The method is based on a representation that incorporates both articulated and non-rigid deformations. ![]() Abstract: We introduce the SCAPE method (Shape Completion and Animation for PEople)-a data-driven method for building a human shape model that spans variation in both subject shape and pose. ![]()
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