000 03621nam a22004935i 4500
001 978-3-319-01538-5
003 DE-He213
005 20200420221303.0
007 cr nn 008mamaa
008 130917s2014 gw | s |||| 0|eng d
020 _a9783319015385
_9978-3-319-01538-5
024 7 _a10.1007/978-3-319-01538-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aSavoye, Yann.
_eauthor.
245 1 0 _aCage-based Performance Capture
_h[electronic resource] /
_cby Yann Savoye.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aX, 141 p. 86 illus., 85 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v509
505 0 _aGeneral Introduction -- Sparse Constraints Over Animatable Subspaces -- Reusing Performance Capture Data -- Toward Non-Rigid Dynamic Cage Capture.
520 _aNowadays, highly-detailed animations of live-actor performances are increasingly easier to acquire and 3D Video has reached considerable attentions in visual media production. In this book, we address the problem of extracting or acquiring and then reusing non-rigid parametrization for video-based animations. At first sight, a crucial challenge is to reproduce plausible boneless deformations while preserving global and local captured properties of dynamic surfaces with a limited number of controllable, flexible and reusable parameters. To solve this challenge, we directly rely on a skin-detached dimension reduction thanks to the well-known cage-based paradigm. First, we achieve Scalable Inverse Cage-based Modeling by transposing the inverse kinematics paradigm on surfaces. Thus, we introduce a cage inversion process with user-specified screen-space constraints. Secondly, we convert non-rigid animated surfaces into a sequence of optimal cage parameters via Cage-based Animation Conversion. Building upon this reskinning procedure, we also develop a well-formed Animation Cartoonization algorithm for multi-view data in term of cage-based surface exaggeration and video-based appearance stylization. Thirdly, motivated by the relaxation of prior knowledge on the data, we propose a promising unsupervised approach to perform Iterative Cage-based Geometric Registration. This novel registration scheme deals with reconstructed target point clouds obtained from multi-view video recording, in conjunction with a static and wrinkled template mesh. Above all, we demonstrate the strength of cage-based subspaces in order to reparametrize highly non-rigid dynamic surfaces, without the need of secondary deformations. To the best of our knowledge this book opens the field of Cage-based Performance Capture.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aImage Processing and Computer Vision.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319015378
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v509
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-01538-5
912 _aZDB-2-ENG
942 _cEBK
999 _c53330
_d53330