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Found 17 papers, 6 papers with code
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.
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.
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.
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.
Guided Conditional Diffusion for Controllable Traffic Simulation
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles.
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.
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.
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.
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.
SpOT: Spatiotemporal Modeling for 3D Object Tracking
In this work, we develop a holistic representation of traffic scenes that leverages both spatial and temporal information of the actors in the scene.
COPILOT: Human-Environment Collision Prediction and Localization from Egocentric Videos
In this work, we introduce the challenging problem of predicting collisions in diverse environments from multi-view egocentric videos captured from body-mounted cameras.
CurveCloudNet: Processing Point Clouds with 1D Structure
Modern depth sensors such as LiDAR operate by sweeping laser-beams across the scene, resulting in a point cloud with notable 1D curve-like structures.
Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion
We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals.
Language-Guided Traffic Simulation via Scene-Level Diffusion
Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development.
NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis
This interaction field guides the sampling of an object-conditioned human motion diffusion model, so as to encourage plausible contacts and affordance semantics.
Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation
To generate composite animations from a multi-track timeline, we propose a new test-time denoising method.
Generating Human Interaction Motions in Scenes with Text Control
Our approach begins with pre-training a scene-agnostic text-to-motion diffusion model, emphasizing goal-reaching constraints on large-scale motion-capture datasets.
