As the demand for 3D content creation is on the rise, it is still notoriously time-consuming for artists to create high quality, detailed 3D models. One significant challenge lies in the intricate detailing of 3D shapes. Traditional synthesis methods generate details procedurally, patch by patch, and then blend them with the underlying geometry or texture. More recently, 3D texture generation methods using pre-trained image generation have shown promise, but they often require a large training set and struggle to produce high-quality details in the generated assets due to view inconsistency, occlusion and distortion.

In this talk, I will introduce my work in transferring details from one single high quality model to arbitrary shapes. I will talk about the algorithm that aims to tackle the two main challenges in texture synthesis on 3D shape: first is the need for the transfer to respond to local geometry, second is to make the texture anisotropic and directional aligned. The algorithm first uses a geometric feature extraction module to develop a probabilistic matching of source and target surface. Then, it generates paired directional fields which will later guide the synthesis process. Lastly, the algorithm directly synthesizes texture on surface by mapping geometric features to the learned texture space through a deep network guided by the directional field. I will also discuss the design choices and challenges inherent in this approach, as well as its potential applications and future developments.

https://docs.google.com/document/d/1HyS0tGw6ZmqgJrrpb6VJ5oKkZZXnTBrLI5ENFlgAvXA/edit