Synthesizing realistic human movements, dynamically responsive to the environment, is a long-standing objective in character animation, with applications in computer vision, sports, and healthcare, for motion prediction and data augmentation. Recent kinematics-based generative motion models offer impressive scalability in modeling extensive motion data, albeit without an interface to reason about and interact with physics. While simulator-in-the-loop learning approaches enable highly physically realistic behaviors, the challenges in training often affect scalability and adoption. We introduce DROP, a novel framework for modeling Dynamics Responses of humans using generative mOtion prior and Projective dynamics. DROP can be viewed as a highly stable, minimalist physics-based human simulator that interfaces with a kinematics-based generative motion prior. Utilizing projective dynamics, DROP allows flexible and simple integration of the learned motion prior as one of the projective energies, seamlessly incorporating control provided by the motion prior with Newtonian dynamics. Serving as a model-agnostic plug-in, DROP enables us to fully leverage recent advances in generative motion models for physics-based motion synthesis. We conduct extensive evaluations of our model across different motion tasks and various physical perturbations, demonstrating the scalability and diversity of responses.

翻译：合成能够动态响应环境的逼真人体运动是角色动画领域的长期目标，其在计算机视觉、体育运动和医疗健康领域具有运动预测与数据增强等应用价值。近年来基于运动学的生成式运动模型在建模大规模运动数据方面展现出卓越的可扩展性，但缺乏与物理世界进行推理和交互的接口。虽然模拟器在环学习方法能够实现高度物理真实的行为，但训练过程中的挑战往往制约其可扩展性和实际应用。我们提出DROP——一种利用生成式运动先验与投影动力学建模人体动力学响应（Dynamics Responses）的全新框架。DROP可被视为一种高度稳定、极简的基于物理的人体模拟器，其与基于运动学的生成式运动先验相连接。通过采用投影动力学，DROP能够灵活简洁地将学习到的运动先验集成作为投影能量之一，将运动先验提供的控制与牛顿动力学无缝融合。作为模型无关的即插即用模块，DROP使我们能够充分利用生成式运动模型的最新进展实现基于物理的运动合成。我们在不同运动任务及多种物理扰动条件下对模型进行广泛评估，验证了其响应行为的可扩展性与多样性。