  Conditional video diffusion models (CDM) have shown promising results for
video synthesis, potentially enabling the generation of realistic
echocardiograms to address the problem of data scarcity. However, current CDMs
require a paired segmentation map and echocardiogram dataset. We present a new
method called Free-Echo for generating realistic echocardiograms from a single
end-diastolic segmentation map without additional training data. Our method is
based on the 3D-Unet with Temporal Attention Layers model and is conditioned on
the segmentation map using a training-free conditioning method based on SDEdit.
We evaluate our model on two public echocardiogram datasets, CAMUS and
EchoNet-Dynamic. We show that our model can generate plausible echocardiograms
that are spatially aligned with the input segmentation map, achieving
performance comparable to training-based CDMs. Our work opens up new
possibilities for generating echocardiograms from a single segmentation map,
which can be used for data augmentation, domain adaptation, and other
applications in medical imaging. Our code is available at
\url{https://github.com/gungui98/echo-free}
