{"ID":3084661,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-06T20:04:15.878508328Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05399","arxiv_id":"2606.05399","title":"UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching","abstract":"Existing feed-forward networks excel at predicting a single set of physical properties from visual appearance, but this point-estimate paradigm fundamentally fails to capture the real world's inherent physical ambiguity. We address this by reframing physics prediction as a task of learning a controllable, continuous distribution of material properties. We introduce UNIPIXIE, a framework trained to predict a continuous and parameterized path of physically plausible material properties from a single visual input. By learning a direct mapping along an object's softest-to-stiffest spectrum on our PIXIEMULTIVERSE dataset, UNIPIXIE allows for controllable generation of diverse, physically valid material fields via a single intuitive parameter. Crucially, UNIPIXIE introduces a novel unified architecture to produce simulation-ready parameters for diverse physics solvers, including continuum-based Material Point Method (MPM), reduced-order deformation based on Linear Blend Skinning (LBS), and anchor-based Spring-Mass systems, addressing a key portability issue in prior work. Experiments show our approach not only generates a rich variety of plausible dynamics but also reduces Young's Modulus prediction error by over 50% against the strongest deterministic baseline, bridging the gap between static point estimates and the continuous nature of physical reality. Project page: https://unipixie.github.io/","short_abstract":"Existing feed-forward networks excel at predicting a single set of physical properties from visual appearance, but this point-estimate paradigm fundamentally fails to capture the real world's inherent physical ambiguity. We address this by reframing physics prediction as a task of learning a controllable, continuous di...","url_abs":"https://arxiv.org/abs/2606.05399","url_pdf":"https://arxiv.org/pdf/2606.05399v1","authors":"[\"Qilin Huang\",\"Quynh Anh Huynh\",\"Long Le\",\"Chen Wang\",\"Chuhao Chen\",\"Ryan Lucas\",\"Eric Eaton\",\"Lingjie Liu\"]","published":"2026-06-03T20:08:23Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}