Reinforcement Learning is an area of Machine Learning focused on how agents can be
trained to make sequential decisions, and achieve a particular goal within an arbitrary …

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 …

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

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 …

Humans perform everyday tasks using a combination of locomotion and manipulation skills.
Building a system that can handle both skills is essential to creating virtual humans. We …

While tremendous advances in visual and auditory realism have been made for virtual and
augmented reality (VR/AR), introducing a plausible sense of physicality into the virtual world …

Motion style transfer is a common method for enriching character animation. Motion style
transfer algorithms are often designed for offline settings where motions are processed in …

We present a physics-based character control framework for synthesizing human-scene
interactions. Recent advances adopt physics simulation to mitigate artifacts produced by …