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Found 8 papers, 5 papers with code
Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior
Scenario generation is formulated as an optimization in the latent space of this traffic model, perturbing an initial real-world scene to produce trajectories that collide with a given planner.
HuMoR: 3D Human Motion Model for Robust Pose Estimation
We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose and shape.
A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries
Grid vertices in a computational fluid dynamics (CFD) domain are viewed as point clouds and used as inputs to a neural network based on the PointNet architecture, which learns an end-to-end mapping between spatial positions and CFD quantities.
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
We propose CaSPR, a method to learn object-centric Canonical Spatiotemporal Point Cloud Representations of dynamically moving or evolving objects.
Contact and Human Dynamics from Monocular Video
Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors that violate physical constraints, such as feet penetrating the ground and bodies leaning at extreme angles.
Predicting the Physical Dynamics of Unseen 3D Objects
Experiments show that we can accurately predict the changes in state for unseen object geometries and initial conditions.
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.
Learning Generalizable Physical Dynamics of 3D Rigid Objects
In this work, we focus on predicting the dynamics of 3D rigid objects, in particular an object's final resting position and total rotation when subjected to an impulsive force.
