Esri’s GIS software is the most powerful mapping & spatia...

" data-medium-file="https://3druck.com/wp-content/uploads/2025/05/3DPeople-PartsVault-12.14.48-copy-300x188.jpg" data-large-file="https://3druck.com/wp-content/uploads/2025/05/3DPeople-PartsVault-12.14.48-copy-1024x640.jpg" /> Mit der Einführung von PartsVault hat das britische 3D-Druck-Dienstleistungsunternehmen 3D People eine Plattform geschaffen, die digitale Bestandsverwaltung mit additiver Fertigung kombiniert. Ziel ist es, Produktionsausfälle durch instabile Lieferketten zu minimieren und Unternehmen eine lokal gesteuerte, bedarfsgerechte Ersatzteilproduktion zu ermöglichen. Die Anwendung stößt insbesondere in Zeiten geopolitischer Spannungen und wachsender Unsicherheit auf breite Resonanz. PartsVault basiert […] Der Beitrag Digitale Ersatzteillagerung mit 3D-Druck: PartsVault von 3D People adressiert Lieferkettenrisiken erschien zuerst auf 3Druck.com - The Independent AM Magazine.

Project website: https://cv.iri.upc-csic.es

Recent advances in 3D human shape estimation build upon parametric representations that model very well the shape of the naked body, but are not appropriate to represent the clothing geometry. In this paper, we present an approach to model dressed humans and predict their geometry from single images. We contribute in three fundamental aspects of the problem, namely, a new dataset, a novel shape parameterization algorithm and an end-to-end deep generative network for predicting shape.First, we present 3DPeople, a large-scale synthetic dataset with 2.5 Million photo-realistic images of 80 subjects performing 70 activities and wearing diverse outfits. Besides providing textured 3D meshes for clothes and body, we annotate the dataset with segmentation masks, skeletons, depth, normal maps and optical flow. All this together makes 3DPeople suitable for a plethora of tasks. We then represent the 3D shapes using 2D geometry images. To build these images we propose a novel spherical area-preserving parameterization algorithm based on the optimal mass transportation method. We show this approach to improve existing spherical maps which tend to shrink the elongated parts of the full body models such as the arms and legs, making the geometry images incomplete. Finally, we design a multi-resolution deep generative network that, given an input image of a dressed human, predicts his/her geometry image (and thus the clothed body shape) in an end-to-end manner. We obtain very promising results in jointly capturing body pose and clothing shape, both for synthetic validation and on the wild images.

Project website: https://cv.iri.upc-csic.es