Proposes residual-based physics-informed coarsening in multigrid GNNs to allocate capacity to high-activity regions for more stable solid mechanics surrogates.
MeshGraphNetRP: Improving Generalization of GNN-based Cloth Simulation , volume =
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
ClothTransformer is a unified latent-space Transformer for cloth simulation that handles body-driven garments, robotic manipulation, and free-fall collisions in one model with 4-9x lower error than prior methods and mesh-resolution independence.
A unified training framework for mesh-based ML surrogates in CFD improves accuracy and long-horizon stability by enforcing spatial derivative consistency via multi-node prediction, using temporal cross-attention correction, and adding 3D rotary positional embeddings.
A deployable Unreal Engine plugin for speech-driven 3D facial animation using ARKit blendshapes, created by converting the MEAD dataset and retraining FaceDiffuser and ProbTalk3D-X, evaluated via user study against MetaHuman and Audio2Face.
citing papers explorer
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Physics-Informed Coarsening for Multigrid Graph Neural Surrogates
Proposes residual-based physics-informed coarsening in multigrid GNNs to allocate capacity to high-activity regions for more stable solid mechanics surrogates.
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ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation
ClothTransformer is a unified latent-space Transformer for cloth simulation that handles body-driven garments, robotic manipulation, and free-fall collisions in one model with 4-9x lower error than prior methods and mesh-resolution independence.
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Mesh Based Simulations with Spatial and Temporal awareness
A unified training framework for mesh-based ML surrogates in CFD improves accuracy and long-horizon stability by enforcing spatial derivative consistency via multi-node prediction, using temporal cross-attention correction, and adding 3D rotary positional embeddings.
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Deploying Speech-Driven 3D Facial Animation in Unreal Engine for Production-Ready Digital Humans
A deployable Unreal Engine plugin for speech-driven 3D facial animation using ARKit blendshapes, created by converting the MEAD dataset and retraining FaceDiffuser and ProbTalk3D-X, evaluated via user study against MetaHuman and Audio2Face.