Academic Journal
Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images
| Title: | Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images |
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| Authors: | Ugrinovic Kehdy, Nicolas, Ruiz Ovejero, Adrià, Agudo Martínez, Antonio, Sanfeliu Cortés, Alberto, Moreno-Noguer, Francesc |
| Contributors: | Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Ministerio de Economía y Competitividad (España), Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] |
| Source: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Digital.CSIC. Repositorio Institucional del CSIC Consejo Superior de Investigaciones Científicas (CSIC) instname |
| Publication Status: | Preprint |
| Publisher Information: | IEEE, 2021. |
| Publication Year: | 2021 |
| Subject Terms: | Optimization, FOS: Computer and information sciences, Image processing--Digital techniques, Gradient descent, Àrees temàtiques de la UPC::Informàtica::Robòtica, Computer Vision and Pattern Recognition (cs.CV), SMPL, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, 3D pose and shape estimation, 03 medical and health sciences, 0302 clinical medicine, Pattern recognition, Pose and shape, 0202 electrical engineering, electronic engineering, information engineering, Imatges--Processament--Tècniques digitals, Computer vision, Informàtica::Robòtica [Àrees temàtiques de la UPC], Multi-person 3D pose estimation |
| Description: | We address the problem of multi-person 3D body pose and shape estimation from a single image. While this problem can be addressed by applying single-person approaches multiple times for the same scene, recent works have shown the advantages of building upon deep architectures that simultaneously reason about all people in the scene in a holistic manner by enforcing, e.g., depth order constraints or minimizing interpenetration among reconstructed bodies. However, existing approaches are still unable to capture the size variability of people caused by the inherent body scale and depth ambiguity. In this work, we tackle this challenge by devising a novel optimization scheme that learns the appropriate body scale and relative camera pose, by enforcing the feet of all people to remain on the ground floor. A thorough evaluation on MuPoTS-3D and 3DPW datasets demonstrates that our approach is able to robustly estimate the body translation and shape of multiple people while retrieving their spatial arrangement, consistently improving current state-of-the-art, especially in scenes with people of very different heights |
| Document Type: | Article Conference object |
| File Description: | application/pdf |
| DOI: | 10.1109/3dv53792.2021.00016 |
| DOI: | 10.48550/arxiv.2111.01884 |
| DOI: | 10.13039/501100003329 |
| DOI: | 10.13039/501100011033 |
| Access URL: | http://arxiv.org/pdf/2111.01884 http://arxiv.org/abs/2111.01884 http://hdl.handle.net/10261/263182 |
| Rights: | IEEE Copyright CC BY NC ND |
| Accession Number: | edsair.doi.dedup.....0625c6034a92702100bb16f3005cb953 |
| Database: | OpenAIRE |
| DOI: | 10.1109/3dv53792.2021.00016 |
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