Neural Implicit Surfaces in Higher Dimension. (arXiv:2201.09636v2 [cs.LG] UPDATED)

This work investigates the use of neural networks admitting high-order
derivatives for modeling dynamic variations of smooth implicit surfaces. For
this purpose, it extends the representation of differentiable neural implicit
surfaces to higher dimensions, which opens up mechanisms that allow to exploit
geometric transformations in many settings, from animation and surface
evolution to shape morphing and design galleries.

The problem is modeled by a $k$-parameter family of surfaces $S_c$, specified
as a neural network function $f : \mathbb{R}^3 \times \mathbb{R}^k …

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