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Quantitative Assessment Protocol for Facial Soft Tissue Volumetric Changes with Stereophotogrammetry
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3D face reconstruction from a single image using a single reference face shape.

Ira Kemelmacher-Shlizerman1, Ronen Basri

  • 1Department of Computer Science and Engineering, University of Washington, Seattle, 98195-2350, USA. kemelmi@cs.washington.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 4, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new 3D face reconstruction method using a single image and one reference 3D face model. The technique molds the reference model to match the input image, achieving accurate 3D face shape recovery.

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Area of Science:

  • Computer Vision
  • 3D Reconstruction
  • Computer Graphics

Background:

  • Human faces share global similarities but exhibit detailed variations.
  • Traditional 3D face reconstruction from single images requires extensive data (e.g., reflectance, lighting, depth).
  • Existing advanced methods use large databases of 3D face models.

Purpose of the Study:

  • To develop a novel, efficient method for 3D face shape recovery from a single image.
  • To overcome limitations of classical and recent 3D reconstruction techniques.
  • To leverage facial similarities for accurate reconstruction using minimal data.

Main Methods:

  • Proposes a method to "mold" a single 3D reference face model using an input 2D image.
  • Assumes Lambertian reflectance and utilizes harmonic representations of lighting.
  • Input: single 2D face image; Reference: single 3D face model.

Main Results:

  • Demonstrates accurate and robust 3D face shape recovery.
  • Tested successfully on both controlled and uncontrolled internet images.
  • Overcomes significant shape differences, including expression, gender, and race variations.

Conclusions:

  • The proposed method offers an effective alternative for single-image 3D face reconstruction.
  • It successfully reconstructs 3D face shapes by deforming a single reference model.
  • The approach is robust across diverse imaging conditions and facial variations.