3 citations · 3 across the 3 of their papers we have counts for
6 papers
PhysibleMDM: Enforcing Inference-Time Physical Feasibility Constraints on Motion Diffusion Models via Noise Optimization
Maximilian Stölzle, David Müller, Agon Serifi +3
Motion Diffusion Models (MDMs) can generate diverse and visually appealing human motion; yet, the resulting trajectories frequently violate physical constraints, limiting their dow…
Walk Like This: Stylized Locomotion for Physics-Based Characters through Example-Driven Diffusion
Joshua Aurand, Agon Serifi, Sammy Christen +4
We present a real-time framework for expressive legged locomotion that enables independent control over velocity and style. Unlike existing data-driven controllers, which typically…
Olaf: Bringing an Animated Character to Life in the Physical World
David Müller, Espen Knoop, Dario Mylonopoulos +4
Animated characters often move in non-physical ways and have proportions that are far from a typical walking robot. This provides an ideal platform for innovation in both mechanica…
Robot Crash Course: Learning Soft and Stylized Falling
Pascal Strauch, David Müller, Sammy Christen +4
Despite recent advances in robust locomotion, bipedal robots operating in the real world remain at risk of falling. While most research focuses on preventing such events, we instea…
AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning
Lucas N. Alegre, Agon Serifi, Ruben Grandia +3
Reinforcement learning (RL) has significantly advanced the control of physics-based and robotic characters that track kinematic reference motion. However, methods typically rely on…
Autonomous Human-Robot Interaction via Operator Imitation
Sammy Christen, David Müller, Agon Serifi +5
Teleoperated robotic characters can perform expressive interactions with humans, relying on the operators' experience and social intuition. In this work, we propose to create auton…