Inverse kinematics (IK) is a core operation in animation, robotics, and biomechanics: given Cartesian constraints, recover joint rotations under a known kinematic tree. In many real-time human avatar pipelines, the available signal per frame is a sparse set of tracked 3D joint positions, whereas animation systems require joint orientations to drive skinning. Recovering full orientations from positions is underconstrained, most notably because twist about bone axes is ambiguous, and classical IK solvers typically rely on iterative optimization that can be slow and sensitive to noisy inputs. We introduce IK-GAT, a lightweight graph-attention network that reconstructs full-body joint orientations from 3D joint positions in a single forward pass. The model performs message passing over the skeletal parent-child graph to exploit kinematic structure during rotation inference. To simplify learning, IK-GAT predicts rotations in a bone-aligned world-frame representation anchored to rest-pose bone frames. This parameterization makes the twist axis explicit and is exactly invertible to standard parent-relative local rotations given the kinematic tree and rest pose. The network uses a continuous 6D rotation representation and is trained with a geodesic loss on SO(3) together with an optional forward-kinematics consistency regularizer. IK-GAT produces animation-ready local rotations that can directly drive a rigged avatar or be converted to pose parameters of SMPL-like body models for real-time and online applications. With 374K parameters and over 650 FPS on CPU, IK-GAT outperforms VPoser-based per-frame iterative optimization without warm-start at significantly lower cost, and is robust to initial pose and input noise

翻译：逆运动学（IK）是动画、机器人学和生物力学中的核心操作：在给定笛卡尔约束条件下，根据已知运动学树恢复关节旋转。在许多实时人体化身管线中，每帧可获取的信号是一组稀疏的3D关节位置，而动画系统需要关节朝向以驱动蒙皮。从位置恢复完整朝向是欠约束的，最显著的问题在于骨骼轴向上的扭转具有歧义性，而经典IK求解器通常依赖迭代优化，这可能导致计算缓慢且对噪声输入敏感。本文提出IK-GAT——一种轻量级图注意力网络，能够通过单次前向传播从3D关节位置重建全身关节朝向。该模型在骨骼父子图上进行消息传递，从而在旋转推理中利用运动学结构。为简化学习过程，IK-GAT采用基于骨骼对齐的世界坐标系表示，该表示锚定于静止姿态的骨骼帧，将扭转轴显式化，并可基于运动学树与静止姿态精确逆变换为标准的父级相对局部旋转。网络采用连续6维旋转表示，并通过SO(3)上的测地损失与可选的（前向运动学一致性正则化项）联合训练。IK-GAT生成的动画就绪局部旋转可直接驱动绑定化身，或转换为SMPL类人体模型姿态参数，适用于实时与在线应用。该模型仅含374K参数，在CPU上运行速度超过650 FPS，以显著更低的计算成本超越了无热启动的基于VPoser的逐帧迭代优化，且对初始姿态与输入噪声具有鲁棒性。