基于JAX的实时逆运动学求解器，高效生成多约束虚拟人动作。
JAX-IK: Real-Time Inverse Kinematics for Generating Multi-Constrained Movements of Virtual Human Characters
- 利用自动微分与即时编译，将正逆运动学视为可微操作。
- 在SMPLX模型上实现实时性能，收敛快、成功率高、计算开销小。
- 适合需要高精度人形动作模拟的图形、机器人与生物力学应用。
在计算机图形学、交互式虚拟环境、机器人学和生物力学等领域，实时生成准确且逼真的虚拟人运动具有重要意义。本文提出一种专为生成类人运动设计的新型实时逆运动学（IK）求解器。借助TensorFlow的自动微分与即时编译能力，该方法能高效处理高自由度的复杂人体骨架。通过将正运动学与逆运动学统一为可微操作，有效解决多约束问题中常见的误差累积与关节限制复杂等难题，显著提升真实感人体运动建模能力。我们在SMPLX人体骨架模型上进行了评估，对比了广泛使用的迭代式IK算法，包括循环坐标下降（CCD）、FABRIK以及非线性优化算法IPOPT。实验涵盖简单末端执行器任务与包含真实关节限制的复杂多约束场景。结果表明，所提求解器具备实时性能，收敛迅速，每轮迭代计算开销极低，且成功率达更高。项目代码已公开于https://github.com/hvoss-techfak/JAX-IK。
原文摘要 · Abstract (English)
Generating accurate and realistic virtual human movements in real-time is of high importance for a variety of applications in computer graphics, interactive virtual environments, robotics, and biomechanics. This paper introduces a novel real-time inverse kinematics (IK) solver specifically designed for realistic human-like movement generation. Leveraging the automatic differentiation and just-in-time compilation of TensorFlow, the proposed solver efficiently handles complex articulated human skeletons with high degrees of freedom. By treating forward and inverse kinematics as differentiable operations, our method effectively addresses common challenges such as error accumulation and complicated joint limits in multi-constrained problems, which are critical for realistic human motion modeling. We demonstrate the solver's effectiveness on the SMPLX human skeleton model, evaluating its performance against widely used iterative-based IK algorithms, like Cyclic Coordinate Descent (CCD), FABRIK, and the nonlinear optimization algorithm IPOPT. Our experiments cover both simple end-effector tasks and sophisticated, multi-constrained problems with realistic joint limits. Results indicate that our IK solver achieves real-time performance, exhibiting rapid convergence, minimal computational overhead per iteration, and improved success rates compared to existing methods. The project code is available at https://github.com/hvoss-techfak/JAX-IK
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