Photo-to-shape material transfer for diverse structures

Author:

Hu Ruizhen1,Su Xiangyu1,Chen Xiangkai1,Van Kaick Oliver2,Huang Hui1

Affiliation:

1. Shenzhen University, China

2. Carleton University, Canada

Abstract

We introduce a method for assigning photorealistic relightable materials to 3D shapes in an automatic manner. Our method takes as input a photo exemplar of a real object and a 3D object with segmentation, and uses the exemplar to guide the assignment of materials to the parts of the shape, so that the appearance of the resulting shape is as similar as possible to the exemplar. To accomplish this goal, our method combines an image translation neural network with a material assignment neural network. The image translation network translates the color from the exemplar to a projection of the 3D shape and the part segmentation from the projection to the exemplar. Then, the material prediction network assigns materials from a collection of realistic materials to the projected parts, based on the translated images and perceptual similarity of the materials. One key idea of our method is to use the translation network to establish a correspondence between the exemplar and shape projection, which allows us to transfer materials between objects with diverse structures. Another key idea of our method is to use the two pairs of (color, segmentation) images provided by the image translation to guide the material assignment, which enables us to ensure the consistency in the assignment. We demonstrate that our method allows us to assign materials to shapes so that their appearances better resemble the input exemplars, improving the quality of the results over the state-of-the-art method, and allowing us to automatically create thousands of shapes with high-quality photorealistic materials. Code and data for this paper are available at https://github.com/XiangyuSu611/TMT.

Funder

NSFC

GD Natural Science Foundation

DEGP Key Project

Guangdong Laboratory of Artificial Intelligence and Digital Economy

GD Talent Plan

Shenzhen Science and Technology Program

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. PSDR-Room: Single Photo to Scene using Differentiable Rendering;SIGGRAPH Asia 2023 Conference Papers;2023-12-10

2. Editing Motion Graphics Video via Motion Vectorization and Transformation;ACM Transactions on Graphics;2023-12-05

3. ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives;ACM Transactions on Graphics;2023-07-26

4. TextureAda: Deep 3D Texture Transfer for Ideation in Product Design Conceptualization;Artificial Intelligence in HCI;2023

5. MatFormer;ACM Transactions on Graphics;2022-07

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