MILI: Multi-person inference from a low-resolution image

Li, Kun, Liu, Yunke, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680 and Yang, Jingyu 2023. MILI: Multi-person inference from a low-resolution image. Fundamental Research 3 (3) , pp. 434-441. 10.1016/j.fmre.2023.02.006

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Abstract

Existing multi-person reconstruction methods require the human bodies in the input image to occupy a considerable portion of the picture. However, low-resolution human objects are ubiquitous due to trade-off between the field of view and target distance given a limited camera resolution. In this paper, we propose an end-to-end multi-task framework for multi-person inference from a low-resolution image (MILI). To perceive more information from a low-resolution image, we use pair-wise images at high resolution and low resolution for training, and design a restoration network with a simple loss for better feature extraction from the low-resolution image. To address the occlusion problem in multi-person scenes, we propose an occlusion-aware mask prediction network to estimate the mask of each person during 3D mesh regression. Experimental results on both small-scale scenes and large-scale scenes demonstrate that our method outperforms the state-of-the-art methods both quantitatively and qualitatively. The code is available at http://cic.tju.edu.cn/faculty/likun/projects/MILI.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Start Date: 2023-02-18
Publisher:	Elsevier
ISSN:	2667-3258
Date of First Compliant Deposit:	6 March 2023
Date of Acceptance:	12 February 2023
Last Modified:	01 Jun 2023 07:45
URI:	https://orca.cardiff.ac.uk/id/eprint/157519

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