Adaptive anisotropic remeshing for cloth simulation

By Rahul Narain, Armin Samii, James F. O'Brien. ACM Transactions on Graphics, 2012

423 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Imitation Learning of Sequential Fabric Smoothing Policies (2019, methodology): “We will utilize higher-ﬁdelity fabric simulators such as ARCSim [28].”
Projective dynamics (2014, methodology): “The connection between PBD and our solver reveals new insights on how PBD relates to traditional approaches based on ﬁnite element methods and Newtonian mechanics.”
Learning Mesh-Based Simulation with Graph Networks (2020, methodology): “[27, 28, 33] changes the mesh by iteratively applying one of three fundamental operations: splitting an edge to reﬁne the mesh, collapsing an edge to coarsen it, and ﬂipping an edge to change orientation and to preserve a sensible aspect ratio of its elements.”
Modeling, learning, perception, and control methods for deformable object manipulation (2021, background): “10 of 16 graphics works demonstrate more complex skills such as manipulating cloth (80) or swinging, wringing, and putting on a scarf (27) using ARCSim (81) and Dynamic Animation and Robotics Toolkits (DART) as simulator.”