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

作　　者：Kun Li Yunke Liu Yu-Kun Lai Jingyu Yang 

机构地区：[1]Tianjin University,Tianjin 300350,China [2]CardiffUniversity,CardiffCF244AG,United Kingdom

出　　处：《Fundamental Research》2023年第3期434-441,共8页自然科学基础研究（英文版）

基　　金：partly supported by the National Natural Science Foundation of China(62122058,62171317,and 62231018).

摘　　要：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-offbetween 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.

关 键 词：Multi-person reconstruction Low-resolution human objects End-to-end Multi-task learning Occlusion-aware prediction 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]