<a href=\"https://research.nvidia.com/labs/sil/projects/freeform/\" rel=\"nofollow\">https://research.nvidia.com/labs/sil/projects/freeform/</a></p>\n<p><video src=\"https://cdn-uploads.huggingface.co/production/uploads/60796959c59d9e1697fa2324/aTb4hV9MjKXJ7-dG-P-D5.mp4\" controls=\"\" class=\"max-w-full!\"></video></p>","updatedAt":"2026-06-02T16:14:11.583Z","author":{"_id":"60796959c59d9e1697fa2324","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60796959c59d9e1697fa2324/wxnDm-p3YgB95NV2p4LGF.png","fullname":"Rishit Dagli","name":"rishitdagli","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":12,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.6266624331474304},"editors":["rishitdagli"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/60796959c59d9e1697fa2324/wxnDm-p3YgB95NV2p4LGF.png"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.29318","authors":[{"_id":"6a1a5eed808ddbc3c7d42f12","name":"Donglai Xiang","hidden":false},{"_id":"6a1a5eed808ddbc3c7d42f13","name":"Vismay Modi","hidden":false},{"_id":"6a1a5eed808ddbc3c7d42f14","name":"Rishit Dagli","hidden":false},{"_id":"6a1a5eed808ddbc3c7d42f15","name":"Ty Trusty","hidden":false},{"_id":"6a1a5eed808ddbc3c7d42f16","name":"Gilles Daviet","hidden":false},{"_id":"6a1a5eed808ddbc3c7d42f17","name":"Anka He Chen","hidden":false},{"_id":"6a1a5eed808ddbc3c7d42f18","name":"Nicholas Sharp","hidden":false},{"_id":"6a1a5eed808ddbc3c7d42f19","name":"David I. W. Levin","hidden":false}],"mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/60796959c59d9e1697fa2324/mj1GSCE9I0wn_nQosBKoH.mp4"],"publishedAt":"2026-05-28T00:00:00.000Z","submittedOnDailyAt":"2026-06-02T00:00:00.000Z","title":"FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes","submittedOnDailyBy":{"_id":"60796959c59d9e1697fa2324","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60796959c59d9e1697fa2324/wxnDm-p3YgB95NV2p4LGF.png","isPro":false,"fullname":"Rishit Dagli","user":"rishitdagli","type":"user","name":"rishitdagli"},"summary":"We present a novel formulation for mesh-free, reduced-order simulation of deformable hyperelastic objects. Existing work in reduced-order elastodynamic simulation represents the input geometry by either meshes, which can be difficult to obtain due to challenges in scanning and triangulating complex shapes, or by neural fields that require per-shape optimization. We propose to adopt a Reproducing Kernel Particle Method (RKPM) representation, which enables the construction of reduced-order skinning weights by solving a generalized eigensystem on the Hessian matrix of the elastic energy. We demonstrate that this formulation not only leads to a 40x training speedup compared with the per-shape optimization of neural fields, but also achieves lower simulation error when evaluated against the converged results of finite element method. We show our simulation results on a wide variety of objects in different representations including meshes and Gaussian splats, as well as the application of our method in the downstream task of robot simulation.","upvotes":1,"discussionId":"6a1a5eed808ddbc3c7d42f1a","githubRepo":"https://github.com/NVIDIAGameWorks/Kaolin","githubRepoAddedBy":"user","ai_summary":"Reduced-order simulation of deformable hyperelastic objects using Reproducing Kernel Particle Method achieves faster training and lower error compared to neural field approaches while supporting various geometric representations.","ai_keywords":["Reproducing Kernel Particle Method","reduced-order simulation","elastic energy","generalized eigensystem","Hessian matrix","finite element method","neural fields","mesh-free","deformable objects","robot simulation"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","githubStars":5100,"organization":{"_id":"60262b67268c201cdc8b7d43","name":"nvidia","fullname":"NVIDIA","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/65df9200dc3292a8983e5017/Vs5FPVCH-VZBipV3qKTuy.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"60796959c59d9e1697fa2324","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60796959c59d9e1697fa2324/wxnDm-p3YgB95NV2p4LGF.png","isPro":false,"fullname":"Rishit Dagli","user":"rishitdagli","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"60262b67268c201cdc8b7d43","name":"nvidia","fullname":"NVIDIA","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/65df9200dc3292a8983e5017/Vs5FPVCH-VZBipV3qKTuy.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.29318.md"}">
FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes
Abstract
Reduced-order simulation of deformable hyperelastic objects using Reproducing Kernel Particle Method achieves faster training and lower error compared to neural field approaches while supporting various geometric representations.
We present a novel formulation for mesh-free, reduced-order simulation of deformable hyperelastic objects. Existing work in reduced-order elastodynamic simulation represents the input geometry by either meshes, which can be difficult to obtain due to challenges in scanning and triangulating complex shapes, or by neural fields that require per-shape optimization. We propose to adopt a Reproducing Kernel Particle Method (RKPM) representation, which enables the construction of reduced-order skinning weights by solving a generalized eigensystem on the Hessian matrix of the elastic energy. We demonstrate that this formulation not only leads to a 40x training speedup compared with the per-shape optimization of neural fields, but also achieves lower simulation error when evaluated against the converged results of finite element method. We show our simulation results on a wide variety of objects in different representations including meshes and Gaussian splats, as well as the application of our method in the downstream task of robot simulation.
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