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3DPeople: modeling the geometry of dressed humans

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10.1109/ICCV.2019.00233
 
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Pumarola Peris, AlbertMés informacióMés informació
Sánchez Riera, JordiMés informacióMés informació
Choi, Gary
Sanfeliu Cortés, AlbertoMés informacióMés informacióMés informació
Moreno-Noguer, FrancescMés informació
Document typeConference report
Defense date2019
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
This work is protected by the corresponding intellectual and industrial property rights. Except where otherwise noted, its contents are licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
ProjectENTENDER EL MOVIMIENTO HUMANO PARA ADAPTAR EL COMPORTAMIENTO DE UN ROBOT (AEI-TIN2017-90086-R)
Abstract
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.
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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
CitationPumarola, A. [et al.]. 3DPeople: modeling the geometry of dressed humans. A: ICCV - IEEE International Conference on Computer Vision. "2019 IEEE/CVF International Conference on Computer Vision (ICCV)". 2019, p. 2242-2251. 
URIhttp://hdl.handle.net/2117/187195
DOI10.1109/ICCV.2019.00233
Publisher versionhttps://ieeexplore.ieee.org/document/9008281
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