  We consider the problem of modifying/replacing the shape style of a real
moving character with those of an arbitrary static real source character.
Traditional solutions follow a pose transfer strategy, from the moving
character to the source character shape, that relies on skeletal pose
parametrization. In this paper, we explore an alternative approach that
transfers the source shape style onto the moving character. The expected
benefit is to avoid the inherently difficult pose to shape conversion required
with skeletal parametrization applied on real characters. To this purpose, we
consider image style transfer techniques and investigate how to adapt them to
3D human shapes. Adaptive Instance Normalisation (AdaIN) and SPADE
architectures have been demonstrated to efficiently and accurately transfer the
style of an image onto another while preserving the original image structure.
Where AdaIN contributes with a module to perform style transfer through the
statistics of the subjects and SPADE contribute with a residual block
architecture to refine the quality of the style transfer. We demonstrate that
these approaches are extendable to the 3D shape domain by proposing a
convolutional neural network that applies the same principle of preserving the
shape structure (shape pose) while transferring the style of a new subject
shape. The generated results are supervised through a discriminator module to
evaluate the realism of the shape, whilst enforcing the decoder to synthesise
plausible shapes and improve the style transfer for unseen subjects. Our
experiments demonstrate an average of $\approx 56\%$ qualitative and
quantitative improvements over the baseline in shape transfer through
optimization-based and learning-based methods.
