  3D human pose estimation from a monocular video has recently seen significant
improvements. However, most state-of-the-art methods are kinematics-based,
which are prone to physically implausible motions with pronounced artifacts.
Current dynamics-based methods can predict physically plausible motion but are
restricted to simple scenarios with static camera view. In this work, we
present D&D (Learning Human Dynamics from Dynamic Camera), which leverages the
laws of physics to reconstruct 3D human motion from the in-the-wild videos with
a moving camera. D&D introduces inertial force control (IFC) to explain the 3D
human motion in the non-inertial local frame by considering the inertial forces
of the dynamic camera. To learn the ground contact with limited annotations, we
develop probabilistic contact torque (PCT), which is computed by differentiable
sampling from contact probabilities and used to generate motions. The contact
state can be weakly supervised by encouraging the model to generate correct
motions. Furthermore, we propose an attentive PD controller that adjusts target
pose states using temporal information to obtain smooth and accurate pose
control. Our approach is entirely neural-based and runs without offline
optimization or simulation in physics engines. Experiments on large-scale 3D
human motion benchmarks demonstrate the effectiveness of D&D, where we exhibit
superior performance against both state-of-the-art kinematics-based and
dynamics-based methods. Code is available at https://github.com/Jeffsjtu/DnD
