Search Results for author: Davis Rempe

Found 16 papers, 6 papers with code

NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis

no code implementations14 Jul 2023 Nilesh Kulkarni, Davis Rempe, Kyle Genova, Abhijit Kundu, Justin Johnson, David Fouhey, Leonidas Guibas

This interaction field guides the sampling of an object-conditioned human motion diffusion model, so as to encourage plausible contacts and affordance semantics.

Motion Synthesis valid

Language-Guided Traffic Simulation via Scene-Level Diffusion

no code implementations10 Jun 2023 Ziyuan Zhong, Davis Rempe, Yuxiao Chen, Boris Ivanovic, Yulong Cao, Danfei Xu, Marco Pavone, Baishakhi Ray

Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development.

Language Modelling Large Language Model

Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion

no code implementations CVPR 2023 Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan, Kris Kitani, Karsten Kreis, Sanja Fidler, Or Litany

We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals.

Collision Avoidance

CurveCloudNet: Processing Point Clouds with 1D Structure

no code implementations21 Mar 2023 Colton Stearns, Davis Rempe, Jiateng Liu, Alex Fu, Sebastien Mascha, Jeong Joon Park, Despoina Paschalidou, Leonidas J. Guibas

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.

COPILOT: Human-Environment Collision Prediction and Localization from Egocentric Videos

no code implementations ICCV 2023 Boxiao Pan, Bokui Shen, Davis Rempe, Despoina Paschalidou, Kaichun Mo, Yanchao Yang, Leonidas J. Guibas

In this work, we introduce the challenging problem of predicting collisions in diverse environments from multi-view egocentric videos captured from body-mounted cameras.

Collision Avoidance Synthetic Data Generation

SpOT: Spatiotemporal Modeling for 3D Object Tracking

no code implementations12 Jul 2022 Colton Stearns, Davis Rempe, Jie Li, Rares Ambrus, Sergey Zakharov, Vitor Guizilini, Yanchao Yang, Leonidas J Guibas

In this work, we develop a holistic representation of traffic scenes that leverages both spatial and temporal information of the actors in the scene.

3D Multi-Object Tracking 3D Object Tracking +1

Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior

no code implementations CVPR 2022 Davis Rempe, Jonah Philion, Leonidas J. Guibas, Sanja Fidler, Or Litany

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.

Autonomous Vehicles

A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries

no code implementations15 Oct 2020 Ali Kashefi, Davis Rempe, Leonidas J. Guibas

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

1 code implementation NeurIPS 2020 Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar, Leonidas J. Guibas

We propose CaSPR, a method to learn object-centric Canonical Spatiotemporal Point Cloud Representations of dynamically moving or evolving objects.

Object Pose Estimation

Contact and Human Dynamics from Monocular Video

1 code implementation ECCV 2020 Davis Rempe, Leonidas J. Guibas, Aaron Hertzmann, Bryan Russell, Ruben Villegas, Jimei Yang

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.

Human Dynamics Pose Estimation

Predicting the Physical Dynamics of Unseen 3D Objects

1 code implementation16 Jan 2020 Davis Rempe, Srinath Sridhar, He Wang, Leonidas J. Guibas

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

1 code implementation NeurIPS 2019 Srinath Sridhar, Davis Rempe, Julien Valentin, Sofien Bouaziz, Leonidas J. Guibas

We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.

3D Shape Reconstruction Object

Learning Generalizable Physical Dynamics of 3D Rigid Objects

no code implementations2 Jan 2019 Davis Rempe, Srinath Sridhar, He Wang, Leonidas J. Guibas

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.

Autonomous Vehicles Object +1

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