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 …

The incredible feats of athleticism demonstrated by humans are made possible in part by a
vast repertoire of general-purpose motor skills, acquired through years of practice and …

Human motion synthesis is an important problem for applications in graphics and gaming,
and even in simulation environments for robotics. Existing methods require accurate motion …

Movement is how people interact with and affect their environment. For realistic character
animation, it is necessary to synthesize such interactions between virtual characters and …

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 …

We present a GAN-based Transformer for general action-conditioned 3D human motion
generation, including not only single-person actions but also multi-person interactive …

In this paper we show how the task of motion tracking for physically simulated characters
can be solved using supervised learning and optimizing a policy directly via back …

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 …

We propose a method for object-aware 3D egocentric pose estimation that tightly integrates
kinematics modeling, dynamics modeling, and scene object information. Unlike prior …

Traditional handcrafted animation often heavily relies on creating keyframes while the in-
betweening is automatically generated through spline-based interpolation. Animators have …