Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks

Geçer, Barış; Lattas, Alexandros; Ploumpis, Stylianos; Deng, Jiankang; Papaioannou, Athanasios; Moschoglou, Stylianos; Zafeiriou, Stefanos

doi:10.1007/978-3-030-58526-6_25

Cited by 46 publications

(29 citation statements)

References 55 publications

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“…While they were able to achieve better reconstruction than linear models, disentangling facial identity and expression was not one of their objectives. Recent works [20,2,16,14,1] have begun to explore the disentanglement of facial identity and expression inside a neural network. The state of the art performance of these methods on standard datasets [13,25] indicate the benefit of learning disentangled representations with neural networks.…”

Section: Related Workmentioning

confidence: 99%

“…As we shall see in more detail in Section 2, recent methods have begun to investigate nonlinear face models using neural networks [28,1,14,20,16,24,3], which can, to some degree, overcome the limitations of linear models. Unfortunately, some of these approaches have thus far sacrificed the human interpretable nature of multi-linear models, as one typically loses semantics when moving to a latent space learned by a deep network.…”

Section: Introductionmentioning

confidence: 99%

“…This is done in a way that fully disentangles the latent space of facial identities from the latent space of expressions. As opposed to existing deep methods [1,14,20,16], the disentanglement is explicitly factored into our architecture and not learned. As a consequence, our method achieves perfect disentanglement between facial identity and expression in its latent space, while still encoding the correlation between identity and expression in shape space, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Deep Face Models

Chandran

Bradley

Groß

et al. 2020

2020 International Conference on 3D Vision (3DV)

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Semantic expression synthesis b) Novel identity synthesis c) Subject & expression specific albedo d) 3D retargetting source target e) 2D landmark-based face tracking Figure 1: We propose semantic deep face models-novel neural architectures for modelling and synthesising 3D human faces with the ability to disentangle identity and expression akin to traditional multi-linear models. We demonstrate several applications of our method including (a) semantic expression synthesis, (b) novel identity synthesis (c) generation of expression specific high resolution albedo maps, (d) 3D facial performance retargeting, and (e) 2D landmark based face tracking.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic Deep Face Models

Chandran

Bradley

Groß

et al. 2020

2020 International Conference on 3D Vision (3DV)

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“…Image generation has attracted broad attention in recent years. Within these works [1][2][3], synthesizing a face from different angles while retaining identity is an important task, because of its wide range of industrial applications, such as video monitoring and face analysis. Recently, this task has been greatly advanced by a number of models of Generative Adversarial Networks.…”

Section: Introductionmentioning

confidence: 99%

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Wei

Li²,

Chen³

et al. 2021

Mathematical Problems in Engineering

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Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.

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“…W Ith the improvement of generative models, such as the Generative Adversarial Networks (GANs) [1]- [3] and variational autoencoders (VAEs) [4], the processing of image-to-image translation have made considerable progresses. The translation includes photo style translation [5], [6], objects dyeing [7]- [9], and also facial synthesis [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network for Multi Facial Attributes Translation

Liu

Feng

2021

IEEE Access

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Recently, Generative Adversarial Network (GAN) based approaches are applied in facial attribute translation. However, many tasks, i.e. multi facial attributes translation and background invariance, are not well handled in the literature. In this paper, we propose a novel GAN-based method that aims to get the target image that performs better within modifying one or more facial attributes in a single model. The model generator learns multi-points by inputting a re-coded transfer vector, ensuring the single model could learn multiple attributes simultaneously. It also optimizes the cycle loss to enhance the efficiency of transferring multi attributes. Moreover, the method uses the adaptive parameter to improve the calculation method of the loss function of the residual image. The results are also compared with the StarGAN v2, which is the current state-of-the-art model to prove the effectiveness and advancedness. Experiments show that our method has a satisfactory performance in multi facial attributes translation.

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Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks

Cited by 46 publications

References 55 publications

Semantic Deep Face Models

Semantic Deep Face Models

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Generative Adversarial Network for Multi Facial Attributes Translation

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