  Radio frequency (RF) signals have been proved to be flexible for human
silhouette segmentation (HSS) under complex environments. Existing studies are
mainly based on a one-shot approach, which lacks a coherent projection ability
from the RF domain. Additionally, the spatio-temporal patterns have not been
fully explored for human motion dynamics in HSS. Therefore, we propose a
two-stage Sequential Diffusion Model (SDM) to progressively synthesize
high-quality segmentation jointly with the considerations on motion dynamics.
Cross-view transformation blocks are devised to guide the diffusion model in a
multi-scale manner for comprehensively characterizing human related patterns in
an individual frame such as directional projection from signal planes.
Moreover, spatio-temporal blocks are devised to fine-tune the frame-level model
to incorporate spatio-temporal contexts and motion dynamics, enhancing the
consistency of the segmentation maps. Comprehensive experiments on a public
benchmark -- HIBER demonstrate the state-of-the-art performance of our method
with an IoU 0.732. Our code is available at https://github.com/ph-w2000/SDM.
