Learning to generate line drawings that convey geometry and semantics-Reference-Cited by-同舟云学术

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Learning to generate line drawings that convey geometry and semantics

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
Short-container-title:

Author:

Chan Caroline¹,Durand Fredo¹,Isola Phillip¹

Affiliation:

1. MIT

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09880227.pdf?arnumber=9880227

Reference86 articles.

1. Rethinking the Inception Architecture for Computer Vision

2. Deep Parametric Shape Predictions Using Distance Fields

3. Mastering Sketching

4. SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation

5. Unaligned Image-to-Image Translation by Learning to Reweight

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1. DALS: Diffusion-Based Artistic Landscape Sketch;Mathematics;2024-01-11

2. [Paper] LineGAN: A Line Drawing Rendering Model with a Focus on Line Density Distribution;ITE Transactions on Media Technology and Applications;2024

3. Content-based Search for Deep Generative Models;SIGGRAPH Asia 2023 Conference Papers;2023-12-10

4. Fine-Tuned but Zero-Shot 3D Shape Sketch View Similarity and Retrieval;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02

5. Sand painting conversion based on detail preservation;Computers & Graphics;2023-10

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