3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation. S Moschoglou, S Ploumpis, MA Nicolaou et al.

Date: May 2019. Source: International Journal of Computer Vision (2020). https://doi.org/10.1007/s11263-020-01329-8. Abstract: Over the past few years, Generative Adversarial Networks (GANs) have garnered increased interest among researchers in Computer Vision, with applications including, but not limited to, image generation, translation, imputation, and super-resolution. Nevertheless, no GAN-based method has been proposed in the literature that can…

MeshMonk: Open-source large-scale intensive 3D phenotyping. JD White, A Ortega-Castrillón, H Matthews et al.

Date: April 2019. Source: Scientific Reports 9, 6085. https://doi.org/10.1038/s41598-019-42533-y. Abstract: Dense surface registration, commonly used in computer science, could aid the biological sciences in accurate and comprehensive quantification of biological phenotypes. However, few toolboxes exist that are openly available, non-expert friendly, and validated in a way relevant to biologists. Here, we report a customizable toolbox…

AMASS: Archive of Motion Capture as Surface Shapes. N Mahmood , N Ghorbani, NF Troje, G Pons-Moll, MJ Black.

Date: April 2019. Source: Cornell University Library – arXiv.org, Computer Vision and Pattern Recognition. Abstract: Large datasets are the cornerstone of recent advances in computer vision using deep learning. In contrast, existing human motion capture (mocap) datasets are small and the motions limited, hampering progress on learning models of human motion. While there are many…

MeshGAN: Non-linear 3D Morphable Models of Faces. S Cheng, M Bronstein, Y Zhou, I Kotsia, M Pantic, S Zafeiriou.

Date: April 2019. Source: Cornell University Library – arXiv.org, Computer Vision and Pattern Recognition. Abstract: Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data. Certain GAN architectures and training methods have demonstrated exceptional performance in generating realistic synthetic images (in particular, of human faces). However, for 3D object, GANs still…

Potential of 3D Surface Imaging for Quantitative Analysis of Fat Grafting. MK Markey, F Merchant, GP Reece et al.

Date: October 2018. Source: 9th 3DBODY.TECH Conference and Expo. October 16-17, 2018. Lugano, Switzerland, USA. Presenter: Mia K Markey Session: Technical Session 3: 3D Face & Body Scanning in Medicine Abstract: Autologous fat grafting is increasingly employed to address volume asymmetry and contour irregularity following breast reconstruction for breast cancer. However, there are no well-established…

Effect of surrogate design on the measured stiffness of snowboarding wrist protectors. C Adams, D James, T Senior, T Allen, N Hamilton.

Date: September 2018. Source: Sports Engineering, Volume 21, Issue 3, pp 217–225. Abstract: In snowboarding, the wrist is the most common injury site, as snowboarders often put their arms out to cushion a fall. This can result in a compressive load through the carpals coupled with wrist hyperextension, leading to ligament sprains or carpal and…

DeepWrinkles: Accurate and Realistic Clothing Modeling. Zorah Lähner, Daniel Cremers, Tony Tung.

Date: September 2018. Source: 15th European Conference on Computer Vision (ECCV), Munich, Germany. Abstract: We present a novel method to generate accurate and realistic clothing deformation from real data capture. Previous methods for realistic cloth modeling mainly rely on intensive computation of physics-based simulation (with numerous heuristic parameters), while models reconstructed from visual observations typically…

Generating 3D Faces using Convolutional Mesh Autoencoders. A Ranjan, T Bolkart, S Sanyal, MJ Black.

Date: September 2018. Source: 15th European Conference on Computer Vision (ECCV), Munich, Germany, (pp 704-720). Abstract: Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of…

A scale space approach for exploring structure in spherical data. V Vuollo, L Holmström.

Date: September 2018. Source: Computational Statistics & Data Analysis, Volume 125, pp 57-69. Highlights: • A novel scale space technique for analyzing spherical data is proposed. • Distributions of normal vector directions computed from a 3dMDhead image are analyzed. • A movie is a convenient way to explore the maps included in a SphereSiZer atlas.…