Coherence and Identity Learning for Arbitrary-length Face Video Generation
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings of ICPR 2020 |
Subtitle of host publication | 25th International Conference on Pattern Recognition |
Publisher | Institute of Electrical and Electronics Engineers, Inc. |
Pages | 915-922 |
ISBN (electronic) | 978-1-7281-8808-9 |
ISBN (print) | 978-1-7281-8809-6 |
Publication status | Published - Jan 2021 |
Publication series
Name | International Conference on Pattern Recognition |
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Publisher | IEEE |
ISSN (Print) | 1051-4651 |
Conference
Title | 25th International Conference on Pattern Recognition (ICPR2020) |
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Location | Virtual |
Place | Italy |
City | Milan |
Period | 10 - 15 January 2021 |
Link(s)
Abstract
Face synthesis is an interesting yet challenging task in computer vision. It is even much harder to generate a portrait video than a single image. In this paper, we propose a novel video generation framework for synthesizing arbitrary-length face videos without any face exemplar or landmark. To overcome the synthesis ambiguity of face video, we propose a divide-and-conquer strategy to separately address the video face synthesis problem from two aspects, face identity synthesis and rearrangement. To this end, we design a cascaded network which contains three components, Identity-aware GAN (IA-GAN), Face Coherence Network, and Interpolation Network. IA-GAN is proposed to synthesize photorealistic faces with the same identity from a set of noises. Face Coherence Network is designed to re-arrange the faces generated by IA-GAN while keeping the inter-frame coherence. Interpolation Network is introduced to eliminate the discontinuity between two adjacent frames and improve the smoothness of the face video. Experimental results demonstrate that our proposed network is able to generate face video with high visual quality while preserving the identity. Statistics show that our method outperforms state-of-the-art unconditional face video generative models in multiple challenging datasets.
Citation Format(s)
Coherence and Identity Learning for Arbitrary-length Face Video Generation. / Ye, Shuquan; Han, Chu; Lin, Jiaying et al.
Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition. Institute of Electrical and Electronics Engineers, Inc., 2021. p. 915-922 9412380 (International Conference on Pattern Recognition).
Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition. Institute of Electrical and Electronics Engineers, Inc., 2021. p. 915-922 9412380 (International Conference on Pattern Recognition).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review