Search Results for author:
Found 17 papers, 4 papers with code
Real-Time Simulated Avatar from Head-Mounted Sensors
We present SimXR, a method for controlling a simulated avatar from information (headset pose and cameras) obtained from AR / VR headsets.
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB camera.
PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction
To make up for the lack of dynamic HOI scenarios in this area, we introduce the BallPlay dataset that contains eight whole-body basketball skills.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
Universal Humanoid Motion Representations for Physics-Based Control
We close this gap by significantly increasing the coverage of our motion representation space.
Perpetual Humanoid Control for Real-time Simulated Avatars
We present a physics-based humanoid controller that achieves high-fidelity motion imitation and fault-tolerant behavior in the presence of noisy input (e. g. pose estimates from video or generated from language) and unexpected falls.
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.
Learning Human Dynamics in Autonomous Driving Scenarios
In this work, we propose a holistic framework for learning physically plausible human dynamics from real driving scenarios, narrowing the gap between real and simulated human behavior in safety-critical applications.
From Universal Humanoid Control to Automatic Physically Valid Character Creation
Second, we use a design-and-control framework to optimize a humanoid's physical attributes to find body designs that can better imitate the pre-specified human motion sequence(s).
Embodied Scene-aware Human Pose Estimation
Since 2D third-person observations are coupled with the camera pose, we propose to disentangle the camera pose and use a multi-step projection gradient defined in the global coordinate frame as the movement cue for our embodied agent.
Ranked #306 on
3D Human Pose Estimation
on Human3.6M
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
Specifically, we learn a conditional policy that, in an episode, first applies a sequence of transform actions to modify an agent's skeletal structure and joint attributes, and then applies control actions under the new design.
Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation
By comparing the pose instructed by the kinematic model against the pose generated by the dynamics model, we can use their misalignment to further improve the kinematic model.
Egocentric Pose Estimation
Human-Object Interaction Detection
+2
Kinematics-Guided Reinforcement Learning for Object-Aware 3D Ego-Pose Estimation
We propose a method for incorporating object interaction and human body dynamics into the task of 3D ego-pose estimation using a head-mounted camera.
3D Human Motion Estimation via Motion Compression and Refinement
Experiments show that our method produces both smooth and accurate 3D human pose and motion estimates.
Ranked #14 on
3D Human Pose Estimation
on 3DPW
(Acceleration Error metric, using extra
training data)
Learning Shape Representations for Clothing Variations in Person Re-Identification
To tackle the re-ID problem in the context of clothing changes, we propose a novel representation learning model which is able to generate a body shape feature representation without being affected by clothing color or patterns.
Cross-Domain 3D Equivariant Image Embeddings
This embedding encodes images with 3D shape properties and is equivariant to 3D rotations of the observed object.
Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices
Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power.
