Text2Tex: Text-driven Texture Synthesis via Diffusion Models-Reference-Cited by-同舟云学术

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Text2Tex: Text-driven Texture Synthesis via Diffusion Models

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Chen Dave Zhenyu¹,Siddiqui Yawar¹,Lee Hsin-Ying²,Tulyakov Sergey²,Nießner Matthias¹

Affiliation:

1. Technical University of Munich

2. Snap Research

Funder

Scan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10377726.pdf?arnumber=10377726

Reference68 articles.

1. 3DAvatarGAN: Bridging Domains for Personalized Editable Avatars

2. Learning representations and generative models for 3d point clouds;Achlioptas

3. Demystifying mmd gans;Bińkowski

4. Efficient Geometry-aware 3D Generative Adversarial Networks

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1. Directional Texture Editing for 3D Models;Computer Graphics Forum;2024-09-02

2. EASI-Tex: Edge-Aware Mesh Texturing from Single Image;ACM Transactions on Graphics;2024-07-19

3. DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models;ACM Transactions on Graphics;2024-07-19

4. CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets;ACM Transactions on Graphics;2024-07-19

5. High-Fidelity 3D Model Generation with Relightable Appearance from Single Freehand Sketches and Text Guidance;2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW);2024-07-15

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