用频域卡尔曼滤波消除动作预测的抖动，让动作更平滑自然。
KHMP: Frequency-Domain Kalman Refinement for High-Fidelity Human Motion Prediction
- 在DCT频域用自适应卡尔曼滤波抑制高频噪声
- 在Human3.6M和HumanEva-I上达到最先进精度
- 适合需要真实物理动作的生成任务
随机人体动作预测旨在从观测序列生成多样且合理的未来动作。尽管生成模型取得进展，现有方法常产生高频抖动和时间不连续的问题。为此，我们提出KHMP框架，通过在DCT域应用自适应卡尔曼滤波，生成高保真动作预测。将高频DCT系数视为频率索引的噪声信号，卡尔曼滤波递归抑制噪声同时保留动作细节。其噪声参数根据估计的信噪比（SNR）动态调整，对抖动预测进行强去噪，对清晰动作则保守过滤。该优化结合训练时的物理约束（时间平滑性和关节角度限制），将生物力学原理融入生成模型。实验在Human3.6M和HumanEva-I数据集上表明，KHMP实现最先进精度，有效消除抖动，生成平滑且符合物理规律的动作。
原文摘要 · Abstract (English)
Stochastic human motion prediction aims to generate diverse, plausible futures from observed sequences. Despite advances in generative modeling, existing methods often produce predictions corrupted by high-frequency jitter and temporal discontinuities. To address these challenges, we introduce KHMP, a novel framework featuring an adaptiveKalman filter applied in the DCT domain to generate high-fidelity human motion predictions. By treating high-frequency DCT coefficients as a frequency-indexed noisy signal, the Kalman filter recursively suppresses noise while preserving motion details. Notably, its noise parameters are dynamically adjusted based on estimated Signal-to-Noise Ratio (SNR), enabling aggressive denoising for jittery predictions and conservative filtering for clean motions. This refinement is complemented by training-time physical constraints (temporal smoothness and joint angle limits) that encode biomechanical principles into the generative model. Together, these innovations establish a new paradigm integrating adaptive signal processing with physics-informed learning. Experiments on the Human3.6M and HumanEva-I datasets demonstrate that KHMP achieves state-of-the-art accuracy, effectively mitigating jitter artifacts to produce smooth and physically plausible motions.
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