Preserving Semantic and Temporal Consistency for Unpaired Video-to-Video Translation
| Autor: | Donghyeon Cho, Sanghyun Woo, KwanYong Park, In So Kweon, Dahun Kim |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science media_common.quotation_subject Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 020207 software engineering Pattern recognition 02 engineering and technology Conditional probability distribution Translation (geometry) Task (project management) Domain (software engineering) Multimedia (cs.MM) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Artificial intelligence Adaptation (computer science) business Computer Science - Multimedia media_common |
| Zdroj: | ACM Multimedia |
| Popis: | In this paper, we investigate the problem of unpaired video-to-video translation. Given a video in the source domain, we aim to learn the conditional distribution of the corresponding video in the target domain, without seeing any pairs of corresponding videos. While significant progress has been made in the unpaired translation of images, directly applying these methods to an input video leads to low visual quality due to the additional time dimension. In particular, previous methods suffer from semantic inconsistency (i.e., semantic label flipping) and temporal flickering artifacts. To alleviate these issues, we propose a new framework that is composed of carefully-designed generators and discriminators, coupled with two core objective functions: 1) content preserving loss and 2) temporal consistency loss. Extensive qualitative and quantitative evaluations demonstrate the superior performance of the proposed method against previous approaches. We further apply our framework to a domain adaptation task and achieve favorable results. Accepted by ACM Multimedia(ACM MM) 2019 |
| Databáze: | OpenAIRE |
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