  In the realm of 3D computer vision, parametric models have emerged as a
ground-breaking methodology for the creation of realistic and expressive 3D
avatars. Traditionally, they rely on Principal Component Analysis (PCA), given
its ability to decompose data to an orthonormal space that maximally captures
shape variations. However, due to the orthogonality constraints and the global
nature of PCA's decomposition, these models struggle to perform localized and
disentangled editing of 3D shapes, which severely affects their use in
applications requiring fine control such as face sculpting. In this paper, we
leverage diffusion models to enable diverse and fully localized edits on 3D
meshes, while completely preserving the un-edited regions. We propose an
effective diffusion masking training strategy that, by design, facilitates
localized manipulation of any shape region, without being limited to predefined
regions or to sparse sets of predefined control vertices. Following our
framework, a user can explicitly set their manipulation region of choice and
define an arbitrary set of vertices as handles to edit a 3D mesh. Compared to
the current state-of-the-art our method leads to more interpretable shape
manipulations than methods relying on latent code state, greater localization
and generation diversity while offering faster inference than optimization
based approaches. Project page: https://rolpotamias.github.io/Shapefusion/
