Face Depth Estimation With Conditional Generative Adversarial Networks

Arslan, Abdullah Taha; Seke, Erol

doi:10.1109/access.2019.2898705

Cited by 18 publications

(10 citation statements)

References 25 publications

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“…This approach may not be regarded as a fully unsupervised method and requires availability or construction of a synthetic dataset. [6] consider a conditional GAN (CGAN) [28] for solving single-image face depth synthesis. Nevertheless, CGANs rely on paired data since the adversarial part estimates the plausibility of an input-output pair.…”

Section: Depth Estimation Using Ganmentioning

confidence: 99%

“…With the advent of virtual and augmented reality applications, single-image pose estimation and 3D reconstruction of human bodies or body parts received a great amount of attention in the research field of computer vision [18]. 3D information on human faces provides additional benefits for face recognition or detection systems [6]. The Texas-3DFRD [12] and the Bosphorus-3DFA [11] are known representatives of paired face RGB-depth data of high quality and include a variety of head poses and emotional expressions.…”

Section: A 3d Databases -An Overviewmentioning

confidence: 99%

“…Real-time depth inference of a given object is a highly important computer vision task which can be applied in various robotic tasks such as simultaneous localization and mapping [1,2,3] as well as autonomous quality inspection in industrial applications [4,5]. As the popularity of VR applications has continued to grow, instant depth estimation has also become an integral part of modeling complex 3D information out of single 2D images of human faces [6,7] or body parts [8,9,10]. Depth information about an object can be directly obtained from sensors for optical distance measurement.…”

Section: Introductionmentioning

confidence: 99%

“…Although DCNNs are a promising approach that succeed on such transfer tasks, they are commonly based on large amounts of training data, and generation and acquisition can be a demanding task. In the supervised setting in particular, DCNNs make use of paired training data during network parameter optimization, i.e., the network is provided with a single-view RGB and corresponding per-pixel depth [6,10,14,15]. Since large scale dense depth profiles are not abundant in many applications, supervised approaches are not feasible for these objects.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction

Angermann¹,

Schwab²,

Haltmeier³

et al. 2022

Preprint

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Real-time estimation of actual object depth is a module that is essential to performing various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment of machinery parts. During the last decade of machine learning, extensive deployment of deep learning methods to computer vision tasks has yielded approaches that succeed in achieving realistic depth synthesis out of a simple RGB modality. While most of these models are based on paired depth data or availability of video sequences and stereo images, methods for single-view depth synthesis in a fully unsupervised setting have hardly been explored. This study presents the most recent advances in the field of generative neural networks, leveraging them to perform fully unsupervised single-shot depth synthesis. Two generators for RGB-to-depth and depth-to-RGB transfer are implemented and simultaneously optimized using the Wasserstein-1 distance and a novel perceptual reconstruction term. To ensure that the proposed method is plausible, we comprehensively evaluate the models using industrial surface depth data as well as the Texas 3D Face Recognition Database and the SURREAL dataset that records body depth. The success observed in this study suggests the great potential for unsupervised single-shot depth estimation in real-world applications.

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Section: Depth Estimation Using Ganmentioning

confidence: 99%

Section: A 3d Databases -An Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction

Angermann¹,

Schwab²,

Haltmeier³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…The research community has explored the monocular depth estimation task using only a single image which is much more straightforward and suitable for consumer applications. The credit goes to significant advances in machine learning-based networks [16]- [20]. In the first part of the paper, we have given a detailed evaluation of publicly available facial depth datasets and widely used loss functions in facial depth estimation networks, thus to better understand the problem of facial depth maps.…”

Section: Introductionmentioning

confidence: 99%

Towards Monocular Neural Facial Depth Estimation: Past, Present, and Future

et al. 2022

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This article contains all of the information needed to conduct a study on monocular facial depth estimation problems. A brief literature review and applications on facial depth map research were offered first, followed by a comprehensive evaluation of publicly available facial depth datasets and widely used loss functions. The key properties and characteristics of each facial depth map dataset are described and evaluated. Furthermore, facial depth maps loss functions are briefly discussed, which will make it easier to train neural facial depth models on a variety of datasets for both short-and long-range depth maps. The network's design and components are essential, but its effectiveness is largely determined by how it is trained, which necessitates a large dataset and a suitable loss function. Implementation details of how neural depth networks work and their corresponding evaluation matrices are presented and explained. In addition, an SoA neural model for facial depth estimation is proposed, along with a detailed comparison evaluation and, where feasible, direct comparison of facial depth estimation methods to serve as a foundation for a proposed model that is utilized. The model employed shows better performance compared with current state-of-the-art methods when tested across four datasets. The new loss function used in the proposed method helps the network to learn the facial regions resulting in an accurate depth prediction. The network is trained on synthetic human facial depth datasets whereas for validation purposes real as well as synthetic facial images are used. The results prove that the trained network outperforms current state-of-the-art networks performances, thus setting up a new baseline method for facial depth estimations.

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