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- Publisher Website: 10.1007/978-3-030-20876-9_40
- Scopus: eid_2-s2.0-85066941563
- WOS: WOS:000492905500040
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Conference Paper: ReCoNet: Real-time Coherent Video Style Transfer Network
Title | ReCoNet: Real-time Coherent Video Style Transfer Network |
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Authors | |
Keywords | Video style transfer Optical flow Real-time processing |
Issue Date | 2019 |
Publisher | Springer. The Proceedings' web site is located at https://link.springer.com/book/10.1007/978-3-030-20876-9 |
Citation | Proceedings of the 14th Asian Conference on Computer Vision (ACCV), Perth, Australia, 2-6 December 2018. In Computer Vision – ACCV 2018, pt. 6, p. 637-653. Cham: Springer, 2019 How to Cite? |
Abstract | Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos. Some video style transfer models have been proposed to improve temporal consistency, yet they fail to guarantee fast processing speed, nice perceptual style quality and high temporal consistency at the same time. In this paper, we propose a novel real-time video style transfer model, ReCoNet, which can generate temporally coherent style transfer videos while maintaining favorable perceptual styles. A novel luminance warping constraint is added to the temporal loss at the output level to capture luminance changes between consecutive frames and increase stylization stability under illumination effects. We also propose a novel feature-map-level temporal loss to further enhance temporal consistency on traceable objects. Experimental results indicate that our model exhibits outstanding performance both qualitatively and quantitatively. |
Description | Oral Session O6: Vision and Language, semantics, and low-level vision - no. O6-1: 523 Revised Selected Papers |
Persistent Identifier | http://hdl.handle.net/10722/271319 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
ISI Accession Number ID | |
Series/Report no. | Lecture Notes in Computer Science (LNCS), v. 11366 |
DC Field | Value | Language |
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dc.contributor.author | Gao, C | - |
dc.contributor.author | Gu, D | - |
dc.contributor.author | Zhang, F | - |
dc.contributor.author | Yu, Y | - |
dc.date.accessioned | 2019-06-24T01:07:33Z | - |
dc.date.available | 2019-06-24T01:07:33Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of the 14th Asian Conference on Computer Vision (ACCV), Perth, Australia, 2-6 December 2018. In Computer Vision – ACCV 2018, pt. 6, p. 637-653. Cham: Springer, 2019 | - |
dc.identifier.isbn | 978-3-030-20875-2 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/271319 | - |
dc.description | Oral Session O6: Vision and Language, semantics, and low-level vision - no. O6-1: 523 | - |
dc.description | Revised Selected Papers | - |
dc.description.abstract | Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos. Some video style transfer models have been proposed to improve temporal consistency, yet they fail to guarantee fast processing speed, nice perceptual style quality and high temporal consistency at the same time. In this paper, we propose a novel real-time video style transfer model, ReCoNet, which can generate temporally coherent style transfer videos while maintaining favorable perceptual styles. A novel luminance warping constraint is added to the temporal loss at the output level to capture luminance changes between consecutive frames and increase stylization stability under illumination effects. We also propose a novel feature-map-level temporal loss to further enhance temporal consistency on traceable objects. Experimental results indicate that our model exhibits outstanding performance both qualitatively and quantitatively. | - |
dc.language | eng | - |
dc.publisher | Springer. The Proceedings' web site is located at https://link.springer.com/book/10.1007/978-3-030-20876-9 | - |
dc.relation.ispartof | 14th Asian Conference on Computer Vision (ACCV), 2018 | - |
dc.relation.ispartof | Computer Vision – ACCV 2018 | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science (LNCS), v. 11366 | - |
dc.subject | Video style transfer | - |
dc.subject | Optical flow | - |
dc.subject | Real-time processing | - |
dc.title | ReCoNet: Real-time Coherent Video Style Transfer Network | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Yu, Y: yzyu@cs.hku.hk | - |
dc.identifier.authority | Yu, Y=rp01415 | - |
dc.identifier.doi | 10.1007/978-3-030-20876-9_40 | - |
dc.identifier.scopus | eid_2-s2.0-85066941563 | - |
dc.identifier.hkuros | 297944 | - |
dc.identifier.volume | 6 | - |
dc.identifier.spage | 637 | - |
dc.identifier.epage | 653 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.identifier.isi | WOS:000492905500040 | - |
dc.publisher.place | Cham | - |
dc.identifier.issnl | 0302-9743 | - |