Dynamic point cloud pretraining is still dominated by masked reconstruction objectives. However, these objectives inherit two key limitations. Existing methods inject ground-truth tube centers as decoder positional embeddings, causing spatio-temporal positional leakage. Moreover, they supervise inter-frame motion with deterministic proxy targets that systematically discard distributional structure by collapsing multimodal trajectory uncertainty into conditional means. To address these limitations, we propose Diffusion Masked Pretraining (DiMP), a unified self-supervised framework for dynamic point clouds. DiMP introduces diffusion modeling into both positional inference and motion learning. It first applies forward diffusion noise only to masked tube centers, then predicts clean centers from visible spatio-temporal context. This removes positional leakage while preserving visible coordinates as clean temporal anchors. DiMP also reformulates point-wise inter-frame displacement supervision as a DDPM noise-prediction objective conditioned on decoded representations. This design drives the encoder to target the full conditional distribution of plausible motions under a variational surrogate, rather than collapsing to a single deterministic estimate. Extensive experiments demonstrate that DiMP consistently improves downstream accuracy over the backbone alone, with absolute gains of 11.21% on offline action segmentation and 13.65% under causally constrained online inference.Codes are available at https://github.com/InitalZ/DiMP.git.

翻译：动态点云预训练仍以掩码重建目标为主导。然而，这些目标存在两个关键局限性。现有方法将真实管中心作为解码器位置嵌入注入，导致时空位置泄漏。此外，它们使用确定性代理目标监督帧间运动，这种目标通过将多模态轨迹不确定性坍缩为条件均值，系统性地丢弃了分布结构。为解决这些问题，我们提出扩散掩码预训练（DiMP）——一种面向动态点云的统一自监督框架。DiMP将扩散建模引入位置推断和运动学习两个环节。它首先仅对掩码管中心施加前向扩散噪声，然后从可见时空上下文中预测干净中心。这消除了位置泄漏，同时保留了可见坐标作为干净的时序锚点。DiMP还将逐点帧间位移监督重新表述为以解码表示作为条件的DDPM噪声预测目标。该设计驱动编码器通过变分代理目标逼近合理运动的全条件分布，而非坍缩为单一确定性估计。大量实验表明，DiMP在骨干网络上持续提升下游精度：离线动作分割绝对提升11.21%，因果约束在线推理绝对提升13.65%。代码开源于https://github.com/InitalZ/DiMP.git。