{"type":"ScholarlyArticle","title":"PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging","authors":[{"name":"Yuwei Li","slug":"yuwei-li","given":"Yuwei","family":"Li","affiliation":""},{"name":"Minye Wu","slug":"minye-wu","given":"Minye","family":"Wu","affiliation":""},{"name":"Yuyao Zhang","slug":"yuyao-zhang","given":"Yuyao","family":"Zhang","affiliation":""},{"name":"Lan Xu","slug":"lan-xu","given":"Lan","family":"Xu","affiliation":""},{"name":"Jingyi Yu","slug":"jingyi-yu","given":"Jingyi","family":"Yu","affiliation":""}],"abstract":"Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose, or texture, without modeling the anatomical attributes like bone, which is essential for realistic hand biomechanics analysis. In this paper, we present PIANO, the first parametric bone model of human hands from MRI data. Our PIANO model is biologically correct, simple to animate, and differentiable, achieving more anatomically precise modeling of the inner hand kinematic structure in a data-driven manner than the traditional hand models based on the outer surface only. Furthermore, our PIANO model can be applied in neural network layers to enable training with a fine-grained semantic loss, which opens up the new task of data-driven fine-grained hand bone anatomic and semantic understanding from MRI or even RGB images. We make our model publicly available.","doi":"10.24963/ijcai.2021/113","url":"https://ijcai.org/proceedings/2021/113","pdf":"https://ijcai.org/proceedings/2021/0113.pdf","markdown":"https://ijcai.org/proceedings/2021/113.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/113","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 I","track":"Main Track","number":113,"pages":{"first":816,"last":822},"keywords":["Computer Vision: Biomedical Image Understanding","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Computer Vision: Statistical Methods and Machine Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}