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Found 8 papers, 2 papers with code
Flexible Motion In-betweening with Diffusion Models
To this end, we propose Conditional Motion Diffusion In-betweening (CondMDI) which allows for arbitrary dense-or-sparse keyframe placement and partial keyframe constraints while generating high-quality motions that are diverse and coherent with the given keyframes.
Physics-based Motion Retargeting from Sparse Inputs
We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies.
Learning to Brachiate via Simplified Model Imitation
Key to our method is the use of a simplified model, a point mass with a virtual arm, for which we first learn a policy that can brachiate across handhold sequences with a prescribed order.
Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels
Vision Transformers (ViT) have recently demonstrated the significant potential of transformer architectures for computer vision.
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction.
Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learning
Learning to locomote is one of the most common tasks in physics-based animation and deep reinforcement learning (RL).
Urban Driving with Conditional Imitation Learning
As our main contribution, we present an end-to-end conditional imitation learning approach, combining both lateral and longitudinal control on a real vehicle for following urban routes with simple traffic.
Learning to Drive in a Day
We demonstrate the first application of deep reinforcement learning to autonomous driving.
