CNS-Edit: 3D Shape Editing via Coupled Neural Shape Optimization
Jingyu Hu Ka-Hei Hui Zhengzhe Liu Hao Zhang Chi-Wing Fu
This paper introduces a new approach based on a coupled representation and a
neural volume optimization to implicitly perform 3D shape editing in latent
space. This work has three innovations. First, we design the coupled neural
shape (CNS) representation for supporting 3D shape editing. This representation
includes a latent code, which captures high-level global semantics of the
shape, and a 3D neural feature volume, which provides a spatial context to
associate with the local shape changes given by the editing. Second, we
formulate the coupled neural shape optimization procedure to co-optimize the
two coupled components in the representation subject to the editing operation.
Last, we offer various 3D shape editing operators, i.e., copy, resize, delete,
and drag, and derive each into an objective for guiding the CNS optimization,
such that we can iteratively co-optimize the latent code and neural feature
volume to match the editing target. With our approach, we can achieve a rich
variety of editing results that are not only aware of the shape semantics but
are also not easy to achieve by existing approaches. Both quantitative and
qualitative evaluations demonstrate the strong capabilities of our approach
over the state-of-the-art solutions.