Multi-View AAM Fitting and Construction

  • Authors:

  • Krishnan Ramnath;Seth Koterba;Jing Xiao;Changbo Hu;Iain Matthews;Simon Baker;Jeffrey Cohn;Takeo Kanade

  • Affiliations:

  • Objectvideo Inc., Reston, USA 20191;The Robotics Institute, Carnegie Mellon University, Pittsburgh, USA 15213;Epson Palo Alto Laboratory, Epson Research & Development, San Jose, USA 95131;The Robotics Institute, Carnegie Mellon University, Pittsburgh, USA 15213;The Robotics Institute, Carnegie Mellon University, Pittsburgh, USA 15213;Microsoft Research, Microsoft Corporation, Redmond, USA 98052;Department of Psychology, University of Pittsburgh, Pittsburgh, USA 15260;The Robotics Institute, Carnegie Mellon University, Pittsburgh, USA 15213

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2008

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Abstract

Active Appearance Models (AAMs) are generative, parametric models that have been successfully used in the past to model deformable objects such as human faces. The original AAMs formulation was 2D, but they have recently been extended to include a 3D shape model. A variety of single-view algorithms exist for fitting and constructing 3D AAMs but one area that has not been studied is multi-view algorithms. In this paper we present multi-view algorithms for both fitting and constructing 3D AAMs. Fitting an AAM to an image consists of minimizing the error between the input image and the closest model instance; i.e. solving a nonlinear optimization problem. In the first part of the paper we describe an algorithm for fitting a single AAM to multiple images, captured simultaneously by cameras with arbitrary locations, rotations, and response functions. This algorithm uses the scaled orthographic imaging model used by previous authors, and in the process of fitting computes, or calibrates, the scaled orthographic camera matrices. In the second part of the paper we describe an extension of this algorithm to calibrate weak perspective (or full perspective) camera models for each of the cameras. In essence, we use the human face as a (non-rigid) calibration grid. We demonstrate that the performance of this algorithm is roughly comparable to a standard algorithm using a calibration grid. In the third part of the paper, we show how camera calibration improves the performance of AAM fitting. A variety of non-rigid structure-from-motion algorithms, both single-view and multi-view, have been proposed that can be used to construct the corresponding 3D non-rigid shape models of a 2D AAM. In the final part of the paper, we show that constructing a 3D face model using non-rigid structure-from-motion suffers from the Bas-Relief ambiguity and may result in a "scaled" (stretched/compressed) model. We outline a robust non-rigid motion-stereo algorithm for calibrated multi-view 3D AAM construction and show how using calibrated multi-view motion-stereo can eliminate the Bas-Relief ambiguity and yield face models with higher 3D fidelity.