{"type":"ScholarlyArticle","title":"PoseGTAC: Graph Transformer Encoder-Decoder with Atrous Convolution for 3D Human Pose Estimation","authors":[{"name":"Yiran Zhu","slug":"yiran-zhu","given":"Yiran","family":"Zhu","affiliation":""},{"name":"Xing Xu","slug":"xing-xu","given":"Xing","family":"Xu","affiliation":""},{"name":"Fumin Shen","slug":"fumin-shen","given":"Fumin","family":"Shen","affiliation":""},{"name":"Yanli Ji","slug":"yanli-ji","given":"Yanli","family":"Ji","affiliation":""},{"name":"Lianli Gao","slug":"lianli-gao","given":"Lianli","family":"Gao","affiliation":""},{"name":"Heng Tao Shen","slug":"heng-tao-shen","given":"Heng Tao","family":"Shen","affiliation":""}],"abstract":"Graph neural networks (GNNs) have been widely used in the 3D human pose estimation task, since the pose representation of a human body can be naturally modeled by the graph structure. Generally, most of the existing GNN-based models utilize the restricted receptive fields of filters and single-scale information, while neglecting the valuable multi-scale contextual information. To tackle this issue, we propose a novel Graph Transformer Encoder-Decoder with Atrous Convolution, named PoseGTAC, to effectively extract multi-scale context and long-range information. In our proposed PoseGTAC model, Graph Atrous Convolution (GAC) and Graph Transformer Layer (GTL), respectively for the extraction of local multi-scale and global long-range information, are combined and stacked in an encoder-decoder structure, where graph pooling and unpooling are adopted for the interaction of multi-scale information from local to global (e.g., part-scale and body-scale). Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that the proposed PoseGTAC model exceeds all previous methods and achieves state-of-the-art performance.","doi":"10.24963/ijcai.2021/188","url":"https://ijcai.org/proceedings/2021/188","pdf":"https://ijcai.org/proceedings/2021/0188.pdf","markdown":"https://ijcai.org/proceedings/2021/188.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/188","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Computer Vision II","track":"Main Track","number":188,"pages":{"first":1359,"last":1365},"keywords":["Computer Vision: Biometrics, Face and Gesture Recognition","Humans and AI: Human-Computer Interaction","Computer Vision: Action Recognition"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}