  Motion mimicking is a foundational task in physics-based character animation.
However, most existing motion mimicking methods are built upon reinforcement
learning (RL) and suffer from heavy reward engineering, high variance, and slow
convergence with hard explorations. Specifically, they usually take tens of
hours or even days of training to mimic a simple motion sequence, resulting in
poor scalability. In this work, we leverage differentiable physics simulators
(DPS) and propose an efficient motion mimicking method dubbed DiffMimic. Our
key insight is that DPS casts a complex policy learning task to a much simpler
state matching problem. In particular, DPS learns a stable policy by analytical
gradients with ground-truth physical priors hence leading to significantly
faster and stabler convergence than RL-based methods. Moreover, to escape from
local optima, we utilize a Demonstration Replay mechanism to enable stable
gradient backpropagation in a long horizon. Extensive experiments on standard
benchmarks show that DiffMimic has a better sample efficiency and time
efficiency than existing methods (e.g., DeepMimic). Notably, DiffMimic allows a
physically simulated character to learn Backflip after 10 minutes of training
and be able to cycle it after 3 hours of training, while the existing approach
may require about a day of training to cycle Backflip. More importantly, we
hope DiffMimic can benefit more differentiable animation systems with
techniques like differentiable clothes simulation in future research.
