  Ultrasound (US) is widely used for its advantages of real-time imaging,
radiation-free and portability. In clinical practice, analysis and diagnosis
often rely on US sequences rather than a single image to obtain dynamic
anatomical information. This is challenging for novices to learn because
practicing with adequate videos from patients is clinically unpractical. In
this paper, we propose a novel framework to synthesize high-fidelity US videos.
Specifically, the synthesis videos are generated by animating source content
images based on the motion of given driving videos. Our highlights are
three-fold. First, leveraging the advantages of self- and fully-supervised
learning, our proposed system is trained in weakly-supervised manner for
keypoint detection. These keypoints then provide vital information for handling
complex high dynamic motions in US videos. Second, we decouple content and
texture learning using the dual decoders to effectively reduce the model
learning difficulty. Last, we adopt the adversarial training strategy with GAN
losses for further improving the sharpness of the generated videos, narrowing
the gap between real and synthesis videos. We validate our method on a large
in-house pelvic dataset with high dynamic motion. Extensive evaluation metrics
and user study prove the effectiveness of our proposed method.
