[{"key":"dc.contributor.author","value":"Suri, Shashwat","language":null},{"key":"dc.date.accessioned","value":"2026-03-19T16:47:04Z","language":null},{"key":"dc.date.available","value":"2026-03-19T16:47:05Z","language":null},{"key":"dc.date.issued","value":"2026","language":"en"},{"key":"dc.identifier.uri","value":"http:\/\/hdl.handle.net\/2429\/93801","language":null},{"key":"dc.description.abstract","value":"Incorporating physics into the capture and prediction of object trajectories has long been a focus in computer vision and graphics, with broad applications in gaming, fashion, and engineering. Recent advances demonstrate the effectiveness of using physics-based loss functions to enhance the accuracy of trajectory estimation. In this work, we introduce a novel workflow consisting of Gaussian-based capture and interpolation of non-rigid media and a differentiable finite element method (FEM) simulation framework to infer constitutive parameters of non-rigid objects from deformation sequences. Our approach accurately recovers these meaningful physical properties from a single deformation sequence, without requiring prior knowledge of the object's material characteristics or the surrounding scene. To support evaluation and benchmarking within this domain, we also release synthetic and real-world data comprising 4D deformation sequences of non-rigid objects, complete with ground-truth physical parameters and collider trajectory annotations.","language":"en"},{"key":"dc.language.iso","value":"eng","language":"en"},{"key":"dc.publisher","value":"University of British Columbia","language":"en"},{"key":"dc.rights","value":"Attribution-NonCommercial-NoDerivatives\r\n4.0 International","language":"en"},{"key":"dc.rights.uri","value":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/","language":"en"},{"key":"dc.title","value":"Vision based material learning through non-rigid deformations","language":"en"},{"key":"dc.type","value":"Text","language":"en"},{"key":"dc.degree.name","value":"Master of Science - MSc","language":"en"},{"key":"dc.degree.discipline","value":"Computer Science","language":"en"},{"key":"dc.degree.grantor","value":"University of British Columbia","language":"en"},{"key":"dc.contributor.supervisor","value":"Pai, D. K. (Dinesh K.)","language":null},{"key":"dc.contributor.supervisor","value":"Rhodin, Helge","language":null},{"key":"dc.date.graduation","value":"2026-05","language":"en"},{"key":"dc.type.text","value":"Thesis\/Dissertation","language":"en"},{"key":"dc.description.affiliation","value":"Science, Faculty of","language":"en"},{"key":"dc.description.affiliation","value":"Computer Science, Department of","language":"en"},{"key":"dc.degree.campus","value":"UBCV","language":"en"},{"key":"dc.description.scholarlevel","value":"Graduate","language":"en"}]