Survey on 3D face reconstruction from uncalibrated images

Morales, Araceli; Piella, Gemma; Sukno, Federico M.

doi:10.1016/j.cosrev.2021.100400

Cited by 47 publications

(22 citation statements)

References 213 publications

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“…with a model trained offline [17]. Numerous methods are proposed [30], but still, the use of 2 regularization (while training the model) is common to virtually all [13], [16], [28], [33], [41], [43], [48], [49], [51]. Robust learning-based pipelines are also difficult to construct.…”

Section: Learning-based Methodsmentioning

confidence: 99%

Inequality-constrained 3D Morphable Face Model Fitting

Sarıyanidi¹,

Zampella²,

Schultz³

et al. 2022

Preprint

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Section: Learning-based Methodsmentioning

confidence: 99%

Inequality-constrained 3D Morphable Face Model Fitting

Sarıyanidi¹,

Zampella²,

Schultz³

et al. 2022

Preprint

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“…Besides using these models for an analysis-by-synthesis approach, there is a series of learned regression-based methods. An overview of these methods is given by Morales et al [55]. In the following, we will discuss the most relevant related work for monocular RGB-based reconstruction methods.…”

Section: Related Workmentioning

confidence: 99%

Towards Metrical Reconstruction of Human Faces

Zielonka¹,

Bolkart²,

Thies³

2022

Preprint

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“…Various methods have been developed to estimate 3D face shapes from one image or multi-views images [ 24 ]. Three different approaches have been applied to reconstruct the 3D shape from 2D information.…”

Section: Introductionmentioning

confidence: 99%

Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients

Nguyen

Dakpe

et al. 2022

Bioengineering

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The 3D reconstruction of an accurate face model is essential for delivering reliable feedback for clinical decision support. Medical imaging and specific depth sensors are accurate but not suitable for an easy-to-use and portable tool. The recent development of deep learning (DL) models opens new challenges for 3D shape reconstruction from a single image. However, the 3D face shape reconstruction of facial palsy patients is still a challenge, and this has not been investigated. The contribution of the present study is to apply these state-of-the-art methods to reconstruct the 3D face shape models of facial palsy patients in natural and mimic postures from one single image. Three different methods (3D Basel Morphable model and two 3D Deep Pre-trained models) were applied to the dataset of two healthy subjects and two facial palsy patients. The reconstructed outcomes were compared to the 3D shapes reconstructed using Kinect-driven and MRI-based information. As a result, the best mean error of the reconstructed face according to the Kinect-driven reconstructed shape is 1.5 ± 1.1 mm. The best error range is 1.9 ± 1.4 mm when compared to the MRI-based shapes. Before using the procedure to reconstruct the 3D faces of patients with facial palsy or other facial disorders, several ideas for increasing the accuracy of the reconstruction can be discussed based on the results. This present study opens new avenues for the fast reconstruction of the 3D face shapes of facial palsy patients from a single image. As perspectives, the best DL method will be implemented into our computer-aided decision support system for facial disorders.

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Survey on 3D face reconstruction from uncalibrated images

Cited by 47 publications

References 213 publications

Inequality-constrained 3D Morphable Face Model Fitting

Inequality-constrained 3D Morphable Face Model Fitting

Towards Metrical Reconstruction of Human Faces

Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients

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