DPE: Disentanglement of Pose and Expression for General Video Portrait Editing
Youxin Pang, Yong Zhang, Weize Quan, Yanbo Fan, Xiaodong Cun, Ying Shan, Dong-Ming Yan
2023年份
27顶会引用
摘要
modified latent codes to images, respectively. Moreover, to guarantee the disentanglement, we propose a bidirectional cyclic training strategy with well-designed constraints. Evaluations demonstrate our method can control pose or expression independently and be used for general video editing.
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引用它的顶会 Paper27
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它引用的顶会 Paper19
- A Lip Sync Expert Is All You Need for Speech to Lip Generation In the WildK. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, C. V. JawaharACM MM 2020 · 被引用 869 次
- Few-Shot Adversarial Learning of Realistic Neural Talking Head ModelsEgor Zakharov, Aliaksandra Shysheya, Egor Burkov, Victor S. LempitskyICCV 2019 · 被引用 687 次
- Learning an animatable detailed 3D face model from in-the-wild imagesYao Feng, Haiwen Feng, Michael J. Black, Timo BolkartSIGGRAPH 2021 · 被引用 662 次
- PIRenderer: Controllable Portrait Image Generation via Semantic Neural RenderingYurui Ren, Ge Li, Yuanqi Chen, Thomas H. Li 等ICCV 2021 · 被引用 284 次
- Latent Image Animator: Learning to Animate Images via Latent Space NavigationYaohui Wang, Di Yang, François Brémond, Antitza DantchevaICLR 2022 · 被引用 219 次
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