Cage-based Performance Capture [electronic resource] /
Savoye, Yann.author.
Nowadays, 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.
Bibliographic Level Mode of Issuance: Monograph
General Introduction -- Sparse Constraints Over Animatable Subspaces -- Reusing Performance Capture Data -- Toward Non-Rigid Dynamic Cage Capture.
Nowadays, 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.
Computational intelligence.
Artificial intelligence.
Optical data processing.
Computational Intelligence.
Artificial Intelligence.
Image Processing and Computer Vision.
Studies in Computational Intelligence