Learning the spatial-temporal structure of body movements is a fundamental problem for
character motion synthesis. In this work, we propose a novel neural network architecture …

Real-time tracking of human body motion is crucial for interactive and immersive
experiences in AR/VR. However, very limited sensor data about the body is available from …

High-quality motion capture datasets are now publicly available, and researchers have used
them to create kinematics-based controllers that can generate plausible and diverse human …

In this paper, we introduce ControlVAE, a novel model-based framework for learning
generative motion control policies based on variational autoencoders (VAE). Our framework …

Legged robots have enormous potential in their range of capabilities, from navigating
unstructured terrains to high-speed running. However, designing robust controllers for highly …

We present a physics-based humanoid controller that achieves high-fidelity motion imitation
and fault-tolerant behavior in the presence of noisy input (eg pose estimates from video or …

In this work, we present MoConVQ, a novel unified framework for physics-based motion
control leveraging scalable discrete representations. Building upon vector quantized …

We present C· ASE, an efficient and effective framework that learns Conditional Adversarial
Skill Embeddings for physics-based characters. C· ASE enables the physically simulated …

Motor neuroscience and physics-based character animation (PBCA) approach human and
humanoid control from different perspectives. The primary goal of PBCA is to control the …

Recent advances in learning reusable motion priors have demonstrated their effectiveness
in generating naturalistic behaviors. In this paper, we propose a new learning framework in …