Learning to Dress 3D People in Generative Clothing. Q Ma, J Yang, A Ranjan, S Pujades, G Pons-Moll, S Tang, MJ Black.

Date: June 2020. Source: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA. Abstract: Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common…

Learning Multi-human Optical Flow. A Ranjan, DT Hoffmann, D Tzionas et al.

Date: January 2020. Source: International Journal of Computer Vision 128, 873–890 (2020). https://doi.org/10.1007/s11263-019-01279-w. Abstract: The optical flow of humans is well known to be useful for the analysis of human action. Recent optical flow methods focus on training deep networks to approach the problem. However, the training data used by them does not cover the…

Digital Twin: Acquiring High-Fidelity 3D Avatar from a Single Image. R Wang, CF Chen, H Peng, X Liu, O Liu, X Li.

Date: December 2019. Source: Cornell University Library – arXiv.org, Computer Vision and Pattern Recognition. Abstract: We present an approach to generate high fidelity 3D face avatar with a high-resolution UV texture map from a single image. To estimate the face geometry, we use a deep neural network to directly predict vertex coordinates of the 3D…

The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking. J Deng, A Roussos, G Chrysos et al.

Date: November 2019. Source: International Journal of Computer Vision, Volume 127, pages 599–624, https://doi.org/10.1007/s11263-018-1134-y. Abstract: In this article, we present the Menpo 2D and Menpo 3D benchmarks, two new datasets for multi-pose 2D and 3D facial landmark localisation and tracking. In contrast to the previous benchmarks such as 300W and 300VW, the proposed benchmarks contain…

Synthesizing Facial Photometries and Corresponding Geometries Using Generative Adversarial Networks. G Shamai, R Slossberg, R Kimmel.

Date: October 2019. Source: ACM Transactions on Multimedia Computing, Communications, and Applications, Article No.: 87 https://doi.org/10.1145/3337067. Abstract: Artificial data synthesis is currently a well-studied topic with useful applications in data science, computer vision, graphics, and many other fields. Generating realistic data is especially challenging, since human perception is highly sensitive to non-realistic appearance. In recent…

Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks. B Gecer, A Lattas, S Ploumpis, et al.

Date: September 2019. Source: Cornell University Library – arXiv.org, Computer Vision and Pattern Recognition. Abstract: Generating realistic 3D faces is of high importance for computer graphics and computer vision applications. Generally, research on 3D face generation revolves around linear statistical models of the facial surface. Nevertheless, these models cannot represent faithfully either the facial texture…

The Influence of Visual Perspective on Body Size Estimation in Immersive Virtual Reality. A Thaler, S Pujades, JK Stefanucci, SH Creem-Regehr, J Tesch, MJ Black, and BJ Mohler

Date: September 2019. Source: ACM Symposium on Applied Perception 2019, University of Barcelona, Spain. Abstract: The creation of realistic self-avatars that users identify with is important for many virtual reality applications. However, current approaches for creating biometrically plausible avatars that represent a particular individual require expertise and are time-consuming. We investigated the visual perception of…

A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. PGM Knoops, A Papaioannou, A Borghi, et al.

Date: September 2019. Source: Scientific Reports 9, 13597 (2019). Abstract: Current computational tools for planning and simulation in plastic and reconstructive surgery lack sufficient precision and are time-consuming, thus resulting in limited adoption. Although computer-assisted surgical planning systems help to improve clinical outcomes, shorten operation time and reduce cost, they are often too complex and…

Capture, Learning, and Synthesis of 3D Speaking Styles. D Cudeiro, T Bolkart, C Laidlaw, A Ranjan, MJ Black.

Date: June 2019. Source: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA. Proceedings Page(s): 10093-10103. Abstract: Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address…

Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision. S Sanyal, T Bolkart, H Feng, MJ Black.

Date: June 2019. Source: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA. Proceedings Page(s): 7755-7764. Abstract: The estimation of 3D face shape from a single image must be robust to variations in lighting, head pose, expression, facial hair, makeup, and occlusions. Robustness requires a large training set of in-the-wild…