Files and links (1)
SubmittedarXiv.org - Non-exclusive license to distribute, Open
Conference proceeding
DIFAI: DIVERSE FACIAL INPAINTING USING STYLEGAN INVERSION
2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, pp 1141-1145
01 Jan 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Image inpainting is an old problem in computer vision that restores occluded regions and completes damaged images. In the case of facial image inpainting, most of the methods generate only one result for each masked image, even though there are other reasonable possibilities. To prevent any potential biases and unnatural constraints stemming from generating only one image, we propose a novel framework for diverse facial inpainting exploiting the embedding space of StyleGAN. Our framework employs pSp encoder and SeFa algorithm to identify semantic components of the StyleGAN embeddings and feed them into our proposed SPARN decoder that adopts region normalization for plausible inpainting. We demonstrate that our proposed method outperforms several state-of-the-art methods.
Metrics
Details
- Title
- DIFAI: DIVERSE FACIAL INPAINTING USING STYLEGAN INVERSION
- Creators
- Dongsik Yoon - University of SeoulJeong-Gi Kwak - University of SeoulYuanming Li - University of SeoulDavid Han - Drexel UniversityHanseok Ko - University of Seoul
- Publication Details
- 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, pp 1141-1145
- Series
- IEEE International Conference on Image Processing ICIP
- Publisher
- IEEE
- Number of pages
- 5
- Grant note
- DMLab
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:001058109501049
- Scopus ID
- 2-s2.0-85146706809
- Other Identifier
- 991021931082104721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic