Presents a consensus-based ADMM distributed version of Affine Body Dynamics that enables scalable multi-node simulation while preserving IPC robustness and non-penetration.
Interactive augmented reality storytelling guided by scene semantics
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
MAS-PNCG accelerates IPC by incrementally updating multilevel MAS preconditioners via Sparse-Input Woodbury, adding Hessian-aware 2D subspace minimization and per-subdomain CCD, achieving up to 5.66x speedup over Newton-PCG baselines.
A multi-modal LM agent is trained to produce vector sketches part-by-part via supervised fine-tuning and process-reward RL on the new ControlSketch-Part dataset with automatic part annotations.
AIvaluateXR benchmarks 17 LLMs across four XR platforms on performance, speed, memory and battery metrics and proposes a 3D Pareto optimality method to identify optimal on-device model-device pairs.
citing papers explorer
-
Distributed Affine Body Dynamics with Adaptive Consensus
Presents a consensus-based ADMM distributed version of Affine Body Dynamics that enables scalable multi-node simulation while preserving IPC robustness and non-penetration.
-
An Efficient Multilevel Preconditioned Nonlinear Conjugate Gradient Method for Incremental Potential Contact
MAS-PNCG accelerates IPC by incrementally updating multilevel MAS preconditioners via Sparse-Input Woodbury, adding Hessian-aware 2D subspace minimization and per-subdomain CCD, achieving up to 5.66x speedup over Newton-PCG baselines.
-
Teaching an Agent to Sketch One Part at a Time
A multi-modal LM agent is trained to produce vector sketches part-by-part via supervised fine-tuning and process-reward RL on the new ControlSketch-Part dataset with automatic part annotations.
-
AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results
AIvaluateXR benchmarks 17 LLMs across four XR platforms on performance, speed, memory and battery metrics and proposes a 3D Pareto optimality method to identify optimal on-device model-device pairs.