  Echocardiography video is a primary modality for diagnosing heart diseases,
but the limited data poses challenges for both clinical teaching and machine
learning training. Recently, video generative models have emerged as a
promising strategy to alleviate this issue. However, previous methods often
relied on holistic conditions during generation, hindering the flexible
movement control over specific cardiac structures. In this context, we propose
an explainable and controllable method for echocardiography video generation,
taking an initial frame and a motion curve as guidance. Our contributions are
three-fold. First, we extract motion information from each heart substructure
to construct motion curves, enabling the diffusion model to synthesize
customized echocardiography videos by modifying these curves. Second, we
propose the structure-to-motion alignment module, which can map semantic
features onto motion curves across cardiac structures. Third, The
position-aware attention mechanism is designed to enhance video consistency
utilizing Gaussian masks with structural position information. Extensive
experiments on three echocardiography datasets show that our method outperforms
others regarding fidelity and consistency. The full code will be released at
https://github.com/mlmi-2024-72/ECM.
