A deep learning-based approach for automated landmark extraction from 3dMD facial photographs was developed and its precision was evaluated. The results showed high precision and consistency in landmark annotation, comparable to manual and semi-automatic annotation methods.
3dMD facial images and deep transfer learning have been firstly combined for evaluating the facial attractiveness in patients undergoing Orthognathic surgery.
We collected a large dataset comprising of textured 3D hand scans. Our hand data were captured during a special exhibition at the Science Museum, London. The capturing apparatus utilized for this task was a 3dMDhand5 system, which is an active stereo photogrammetry software-driven optics-based system, which produces high quality dense meshes.
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Introducing ARCTIC – the first dataset of free-form interactions of hands and articulated objects. ARCTIC has 1.2M images paired with accurate 3D meshes for both hands and for objects that move and deform over time. The dataset also provides hand-object contact information.
Results of this study show that 3dMD photography is a valid alternative to CT for evaluation of head shape in MCS. Its use will provide an objective, quantifiable means of assessing outcomes in a rigorous manner while decreasing radiation exposure in this patient population.
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…
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…
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…
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…