Modeling human motor control and predicting how humans will move in novel environments
is a grand scientific challenge. Researchers in the fields of biomechanics and motor control …

A longstanding goal in character animation is to combine data-driven specification of
behavior with a system that can execute a similar behavior in a physical simulation, thus …

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 …

Many anatomical factors, such as bone geometry and muscle condition, interact to affect
human movements. This work aims to build a comprehensive musculoskeletal model and its …

Data-driven character animation based on motion capture can produce highly naturalistic
behaviors and, when combined with physics simulation, can provide for natural procedural …

Interactive control of self-balancing, physically simulated humanoids is a long standing
problem in the field of real-time character animation. While physical simulation guarantees …

Developing control policies in simulation is often more practical and safer than directly
running experiments in the real world. This applies to policies obtained from planning and …

Human characters with a broad range of natural looking and physically realistic behaviors
will enable the construction of compelling interactive experiences. In this paper, we develop …

Learning locomotion skills is a challenging problem. To generate realistic and smooth
locomotion, existing methods use motion capture, finite state machines or morphology …

The use of deep reinforcement learning allows for high-dimensional state descriptors, but
little is known about how the choice of action representation impacts learning and the …