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Related Experiment Videos

Reflectance from images: a model-based approach for human faces.

Martin Fuchs1, Volker Blanz, Hendrik Lensch

  • 1Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany. mfuchs@mpi-sb.mpg.de

IEEE Transactions on Visualization and Computer Graphics
|May 5, 2005
PubMed
Summary

This study introduces an image-based method to capture facial reflectance without 3D scans. It enables comparing and transferring Bidirectional Reflectance Distribution Function (BRDF) data between different faces using a unified parameterization.

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Area of Science:

  • Computer Vision
  • Computer Graphics
  • Human-Computer Interaction

Background:

  • Accurate acquisition of human face reflectance properties is crucial for realistic rendering and analysis.
  • Existing methods often require 3D scans or lack robust correspondence across different faces and expressions.

Purpose of the Study:

  • To present an image-based framework for acquiring facial reflectance properties without requiring a 3D range scan.
  • To establish a common parameterization for comparing and transferring Bidirectional Reflectance Distribution Function (BRDF) data between faces.

Main Methods:

  • Utilizing a morphable face model to estimate 3D shape and establish point-to-point correspondence across multiple viewpoints and individuals.
  • Compensating for facial deformations (e.g., expressions) during shape estimation.

Related Experiment Videos

  • Applying analytical BRDF models and least-squares fitting to image data for parameter estimation.
  • Locally refining the diffuse component of the BRDF for high detail.

    • Main Results:

    • Successful estimation of 3D face shape and point-to-point correspondence from images.
    • A unified parameterization enabling BRDF data transfer and comparison between different faces.
    • Demonstration of reflectance property acquisition and rendering under novel conditions.

    Conclusions:

    • The proposed framework effectively acquires facial reflectance properties using only images.
    • The common parameterization facilitates cross-individual BRDF analysis and manipulation.
    • The method robustly handles facial deformations, offering a practical solution for realistic face modeling.