We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration -- a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image.

A checkpoint of the trained model can be downloaded here. For further details on how to work with the iRBSM, we refer to our GitHub repository. Please enter your full name, institution, and valid e-mail address to get access. Upon submission, an e-mail containing the download link will be sent to the address provided (please provide an institutional e-mail address if possible--Gmail won't work!).