MagicClay: Sculpting Meshes With Generative Neural Fields
Authors: name: Amir Barda, name: Vladimir G. Kim, name: Noam Aigerman, name: Amit H. Bermano, name: Thibault Groueix

The recent developments in neural fields have brought phenomenal capabilities
to the field of shape generation, but they lack crucial properties, such as
incremental control - a fundamental requirement for artistic work. Triangular
meshes, on the other hand, are the representation of choice for most geometry
related tasks, offering efficiency and intuitive control, but do not lend
themselves to neural optimization. To support downstream tasks, previous art
typically proposes a two-step approach, where first a shape is generated using
neural fields, and then a mesh is extracted for further processing. Instead, in
this paper we introduce a hybrid approach that maintains both a mesh and a
Signed Distance Field (SDF) representations consistently. Using this
representation, we introduce MagicClay - an artist friendly tool for sculpting
regions of a mesh according to textual prompts while keeping other regions
untouched. Our framework carefully and efficiently balances consistency between
the representations and regularizations in every step of the shape
optimization; Relying on the mesh representation, we show how to render the SDF
at higher resolutions and faster. In addition, we employ recent work in
differentiable mesh reconstruction to adaptively allocate triangles in the mesh
where required, as indicated by the SDF. Using an implemented prototype, we
demonstrate superior generated geometry compared to the state-of-the-art, and
novel consistent control, allowing sequential prompt-based edits to the same
mesh for the first time.