3D Human Shape Style Transfer
Joao Regateiro, Edmond Boyer

TL;DR
This paper introduces a novel 3D human shape style transfer method that leverages image style transfer architectures like AdaIN and SPADE, avoiding complex pose-to-shape conversions and improving transfer quality.
Contribution
It adapts image style transfer techniques to 3D human shapes using a neural network that preserves shape pose while transferring style, with supervised realism and plausibility.
Findings
Achieved approximately 56% improvement over baseline methods.
Demonstrated effective style transfer on unseen subjects.
Extended image style transfer architectures to 3D shape domain.
Abstract
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…
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Taxonomy
Topics3D Shape Modeling and Analysis · Human Motion and Animation · Computer Graphics and Visualization Techniques
MethodsSpatially-Adaptive Normalization · Residual Connection · *Communicated@Fast*How Do I Communicate to Expedia? · Batch Normalization · Convolution · Residual Block
