Normal Image Manipulation for Bas-relief Generation with Hybrid Styles

TL;DR

This paper presents a versatile normal-based bas-relief generation method that extends existing techniques by combining frequency manipulation, detail transfer, and single-image input, formulated as a variational problem solved via a screened Poisson equation.

Contribution

It introduces a novel framework that broadens the shape space and style diversity of bas-reliefs, enabling detail transfer and single-image processing with a variational formulation.

Findings

Produces high-quality, diverse bas-relief models

Outperforms classic and state-of-the-art methods

Capable of detail transfer and single-image generation

Abstract

We introduce a normal-based bas-relief generation and stylization method which is motivated by the recent advancement in this topic. Creating bas-relief from normal images has successfully facilitated bas-relief modeling in image space. However, the use of normal images in previous work is often restricted to certain type of operations only. This paper is intended to extend normal-based methods and construct bas-reliefs from normal images in a versatile way. Our method can not only generate a new normal image by combining various frequencies of existing normal images and details transferring, but also build bas-reliefs from a single RGB image and its edge-based sketch image. In addition, we introduce an auxiliary function to represent a smooth base surface and generate a layered global shape. To integrate above considerations into our framework, we formulate the bas- relief generation as a variational problem which can be solved by a screened Poisson equation. Some advantages of our method are that it expands the bas-relief shape space and generates diversified styles of results, and that it is capable of transferring details from one region to other regions. Our method is easy to implement, and produces good-quality bas-relief models. We experiment our method on a range of normal images and it compares favorably to other popular classic and state-of-the-art methods.