Recent developments have created differentiable physics simulators designed
for machine learning pipelines that can be accelerated on a GPU. While these
can simulate biomechanical models, these opportunities have not been exploited
for biomechanics research or markerless motion capture. We show that these
simulators can be used to fit inverse kinematics to markerless motion capture
data, including scaling the model to fit the anthropomorphic measurements of an
individual. This is performed end-to-end with an implicit representation of the
movement trajectory, which is propagated through the forward kinematic model to
minimize the error from the 3D markers reprojected into the images. The
differential optimizer yields other opportunities, such as adding bundle
adjustment during trajectory optimization to refine the extrinsic camera
parameters or meta-optimization to improve the base model jointly over
trajectories from multiple participants. This approach improves the
reprojection error from markerless motion capture over prior methods and
produces accurate spatial step parameters compared to an instrumented walkway
for control and clinical populations.

最近的研究发展提出了可在 GPU 上加速的适用于机器学习流程的可微分物理模拟器。我们展示了这些模拟器可以用于拟合标记点无遮挡运动捕捉数据的反向运动学，包括缩放模型以适应个体的人体测量值。通过隐式表示的运动轨迹，继而通过正向运动学模型传播，从而最小化重新投影到图像中的 3D 标记点的误差。不同的优化器还提供了其他机会，如在轨迹优化期间添加束调整以细化外部相机参数，或者进行元优化以改善基本模型，并联合使用多个参与者的轨迹。这种方法改善了标记点无遮挡运动捕捉的重新投影误差，并产生了与仪器走道的对照和临床人群相比准确的空间步长参数。

可微生物力学打开无标点运动捕捉的机会

Differentiable Biomechanics Unlocks Opportunities for Markerless Motion  Capture

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.

本文提出了一种名为 DiffMimic 的新型运动模仿方法，利用可微物理模拟器 (DPS) 降低了模仿的时间成本和样本成本，并且克服了现有方法中存在的一些问题。实验结果表明，DiffMimic 在标准测试中具有更好的样本和时间效率，使得物理仿真角色可以在较短的时间内学习到 Backflip 和循环，从而为未来更多的可微动画系统，如可微服装仿真，带来更多研究机会。