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Modeling and Similitude01:12

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A Lightweight Monocular 3D Face Reconstruction Method Based on Improved 3D Morphing Models.

Xingyi You1,2, Yue Wang1,2, Xiaohu Zhao1,2

  • 1National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines, China University of Mining and Technology, Xuzhou 221008, China.

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Summary
This summary is machine-generated.

This study introduces Mobile-FaceRNet, an efficient and lightweight network for 3D face reconstruction. It achieves high-fidelity facial texture generation with improved speed and robustness, overcoming limitations of existing 3D Morphing Model methods.

Keywords:
3D face reconstruction3DMMlightweight network

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Area of Science:

  • Computer Vision
  • 3D Graphics
  • Machine Learning

Background:

  • 3D Morphing Model (3DMM)-based methods excel in single-image 3D face reconstruction and texture generation.
  • Deep convolutional neural networks enhance texture generation but increase computational burden and reduce speed.
  • Existing lightweight networks sacrifice reconstruction accuracy for speed.

Purpose of the Study:

  • To develop an efficient and lightweight network for high-fidelity 3D face reconstruction.
  • To improve the computational speed of 3DMM parameter fitting without compromising accuracy.
  • To enhance robustness against variations in pose and occlusion.

Main Methods:

  • Proposed Mobile-FaceRNet, combining depthwise separable convolution and multi-scale representation for 3DMM parameter fitting.
  • Incorporated a residual attention module to focus on critical features for high-fidelity texture reconstruction.
  • Designed a novel perceptual loss function to enforce smoothness and image similarity constraints.

Main Results:

  • Mobile-FaceRNet achieves high-precision 3D face reconstruction with a lightweight architecture.
  • The method demonstrates improved computational speed compared to traditional deep learning approaches.
  • Experimental results confirm robustness to pose variations and occlusions.

Conclusions:

  • Mobile-FaceRNet offers a balanced solution for fast and accurate 3D face reconstruction.
  • The proposed network advances the state-of-the-art in single-image 3D face modeling.
  • This approach is suitable for applications requiring efficient and robust facial reconstruction.