Fine detailed texture learning for 3D meshes with generative models

Date

2023-11-03

Editor(s)

Advisor

Supervisor

Co-Advisor

Co-Supervisor

Instructor

BUIR Usage Stats
22
views
22
downloads

Citation Stats

Series

Abstract

This paper presents a method to achieve fine detailed texture learning for 3D models that are reconstructed from both multi-view and single-view images. The framework is posed as an adaptation problem and is done progressively where in the first stage, we focus on learning accurate geometry, whereas in the second stage, we focus on learning the texture with a generative adversarial network. The contributions of the paper are in the generative learning pipeline where we propose two improvements. First, since the learned textures should be spatially aligned, we propose an attention mechanism that relies on the learnable positions of pixels. Second, since discriminator receives aligned texture maps, we augment its input with a learnable embedding which improves the feedback to the generator. We achieve significant improvements on multi-view sequences from Tripod dataset as well as on single-view image datasets, Pascal 3D+ and CUB. We demonstrate that our method achieves superior 3D textured models compared to the previous works.

Source Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Publisher

Institute of Electrical and Electronics Engineers

Course

Other identifiers

Book Title

Degree Discipline

Degree Level

Degree Name

Citation

Published Version (Please cite this version)

Language

English