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

Li, Kun, Liu, Yunke, Lai, Yu-Kun ORCID: 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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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

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Additional Information: License information from Publisher: LICENSE 1: URL:, 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

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