{"title":"Occluded human pose estimation based on part-aware discrete diffusion priors","authors":"Hongyu Xiao , Hui He , Yifan Xie , Yi Zheng","doi":"10.1016/j.knosys.2025.113272","DOIUrl":null,"url":null,"abstract":"<div><div>In this work, we focus on reconstructing human poses from RGB images, with particular attention given to the ambiguity issues caused by complex scenes such as occlusions. The main challenges we face are twofold: how to reconstruct a complete pose based on limited visible cues and how to handle the uncertainty of occluded parts. To address these issues, our primary approach is to leverage human prior knowledge to ensure the physical plausibility of the reconstructed pose and simulate occluded scenarios through the forward process of the diffusion model, followed by recovering the occluded parts through the reverse process. Specifically, we first train hierarchical encoders, codebooks, and decoders to learn rich pose prior knowledge and then incorporate these priors into a discrete diffusion model with multimodal guidance. We train the network to gradually predict clean discrete pose tokens that are consistent with prior knowledge and ultimately decode them into complete body poses. Extensive experimental results on the COCO and 3DMPB datasets demonstrate that our method achieves state-of-the-art performance compared with previous approaches. The code will be publicly available.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113272"},"PeriodicalIF":7.2000,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705125003193","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In this work, we focus on reconstructing human poses from RGB images, with particular attention given to the ambiguity issues caused by complex scenes such as occlusions. The main challenges we face are twofold: how to reconstruct a complete pose based on limited visible cues and how to handle the uncertainty of occluded parts. To address these issues, our primary approach is to leverage human prior knowledge to ensure the physical plausibility of the reconstructed pose and simulate occluded scenarios through the forward process of the diffusion model, followed by recovering the occluded parts through the reverse process. Specifically, we first train hierarchical encoders, codebooks, and decoders to learn rich pose prior knowledge and then incorporate these priors into a discrete diffusion model with multimodal guidance. We train the network to gradually predict clean discrete pose tokens that are consistent with prior knowledge and ultimately decode them into complete body poses. Extensive experimental results on the COCO and 3DMPB datasets demonstrate that our method achieves state-of-the-art performance compared with previous approaches. The code will be publicly available.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.