Exemplar-Based 3D Portrait Stylization
Fangzhou Han, Shuquan Ye, Mingming He, Menglei Chai, Jing Liao

TL;DR
This paper introduces a novel one-shot framework for 3D portrait style transfer that stylizes both geometry and texture from a single style image, enabling high-quality artistic 3D face models with identity preservation.
Contribution
It is the first to achieve one-shot 3D portrait style transfer with disentangled geometry and texture outputs, requiring only one style image and supporting various graphics applications.
Findings
Outperforms existing methods in style transfer quality.
Robust results across different artistic styles.
Enables multiple 2D and 3D graphics applications.
Abstract
Exemplar-based portrait stylization is widely attractive and highly desired. Despite recent successes, it remains challenging, especially when considering both texture and geometric styles. In this paper, we present the first framework for one-shot 3D portrait style transfer, which can generate 3D face models with both the geometry exaggerated and the texture stylized while preserving the identity from the original content. It requires only one arbitrary style image instead of a large set of training examples for a particular style, provides geometry and texture outputs that are fully parameterized and disentangled, and enables further graphics applications with the 3D representations. The framework consists of two stages. In the first geometric style transfer stage, we use facial landmark translation to capture the coarse geometry style and guide the deformation of the dense 3D face…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Face recognition and analysis · 3D Shape Modeling and Analysis
