Tomorrow’s digital ‘human’ creation will be based on AI learning from authentic real-world data of people recorded physically in action with the utmost 3D fidelity.
We make a step towards automatic realistic garment design and propose Design2Cloth, a high fidelity 3D generative model trained on a real world dataset. The scans of a total of 2010 distinct subjects spanning different ages, body types and ethnicities were captured using a 3dMDbody System.
Explore the true economic benefits of baselining with a ‘near-ground truth’ digital-3D Doppelganger population and how this can create derivative avatar market opportunities beyond gaming, entertainment, and social media interactions.
How 3dMD provides an efficient image capture workflow tool for XR/VR/AR, Computer Vision, AI, Machine Learning, and Human Factors teams to personalize natural human actions, behaviors, gestures, nuances, etc. into digital media interpretation input that will persistently train, wear, and populate the metaverse.
Within the global population we find many differences in the factors that drive fit & comfort in AR, which presents a substantial challenge to design. Danny will discuss several aspects of human anthropometry, along with the concepts of Fit Mapping and Fit Testing as they apply to designing wearable devices for a global audience.
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…