GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction. B Gecer, S Ploumpis, I Kotsia, S Zafeiriou.
Imperial College London trains GANFIT (2019), a photorealistic 3D face GAN, with 3dMD’s real-world MeIn3D dataset.
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.
MPI-IS builds AMASS (2019), the largest unified mocap archive, with 3dMD’s real-world 4D scan data.
MeshGAN: Non-linear 3D Morphable Models of Faces. S Cheng, M Bronstein, Y Zhou, I Kotsia, M Pantic, S Zafeiriou.
Imperial College London trains MeshGAN (2019), a 3D face GAN, with 3dMD’s real-world 12,000-identity dataset.
Intelligent Harvesting of High-Precision 4D Data of Human Subjects in a Personalized Digital Economy. Chris Lane.
Date: October 2018. Source: 9th 3DBODY.TECH Conference and Expo. October 16-17, 2018. Lugano, Switzerland, USA. Presenter: Chris LANE, 3dMD Ltd. Session: Technical Session 10: Full Body Scanning & Processing
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. Z Lähner, D Cremers, T Tung.
Meta Reality Labs builds DeepWrinkles (2018), a clothing deformation model, with 3dMD’s real-world 4D data, presented at ECCV 2018.
Generating 3D Faces using Convolutional Mesh Autoencoders. A Ranjan, T Bolkart, S Sanyal, MJ Black.
MPI-IS trains CoMA (2018), a mesh autoencoder for generating 3D faces, with 3dMD’s real-world 4D facial data.