3D Face Reconstruction From Radar Images
Valentin Braeutigam, Vanessa Wirth, Ingrid Ullmann, Christian, Sch\"u{\ss}ler, Martin Vossiek, Matthias Berking, Bernhard Egger

TL;DR
This paper introduces a novel radar-based 3D face reconstruction method using a model-based autoencoder trained on synthetic data, enabling accurate reconstruction from radar images with potential applications in security and healthcare.
Contribution
It presents a new framework combining CNN encoding and differentiable radar rendering for 3D face reconstruction from radar images, including a synthetic dataset and unsupervised fine-tuning.
Findings
Effective reconstruction on synthetic radar images
Successful application to real radar data with ground truth
Enhanced accuracy through autoencoder fine-tuning
Abstract
The 3D reconstruction of faces gains wide attention in computer vision and is used in many fields of application, for example, animation, virtual reality, and even forensics. This work is motivated by monitoring patients in sleep laboratories. Due to their unique characteristics, sensors from the radar domain have advantages compared to optical sensors, namely penetration of electrically non-conductive materials and independence of light. These advantages of radar signals unlock new applications and require adaptation of 3D reconstruction frameworks. We propose a novel model-based method for 3D reconstruction from radar images. We generate a dataset of synthetic radar images with a physics-based but non-differentiable radar renderer. This dataset is used to train a CNN-based encoder to estimate the parameters of a 3D morphable face model. Whilst the encoder alone already leads to strong…
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Taxonomy
TopicsAdvanced SAR Imaging Techniques · Face recognition and analysis
MethodsSoftmax · Attention Is All You Need
