Advances in 3D Neural Stylization: A Survey

TL;DR

This survey reviews recent advances in neural stylization techniques for 3D data, establishing a taxonomy, evaluating methods, and discussing future challenges and opportunities in AI-driven 3D content creation.

Contribution

It provides a comprehensive taxonomy and benchmark for neural stylization methods applied to 3D data, highlighting recent progress and open challenges.

Findings

Neural stylization enables diverse 3D content creation.

Benchmark results compare mesh and neural field stylization methods.

Insights into design choices and future research directions.

Abstract

Modern artificial intelligence offers a novel and transformative approach to creating digital art across diverse styles and modalities like images, videos and 3D data, unleashing the power of creativity and revolutionizing the way that we perceive and interact with visual content. This paper reports on recent advances in stylized 3D asset creation and manipulation with the expressive power of neural networks. We establish a taxonomy for neural stylization, considering crucial design choices such as scene representation, guidance data, optimization strategies, and output styles. Building on such taxonomy, our survey first revisits the background of neural stylization on 2D images, and then presents in-depth discussions on recent neural stylization methods for 3D data, accompanied by a benchmark evaluating selected mesh and neural field stylization methods. Based on the insights gained from the survey, we highlight the practical significance, open challenges, future research, and potential impacts of neural stylization, which facilitates researchers and practitioners to navigate the rapidly evolving landscape of 3D content creation using modern artificial intelligence.