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- W4312365317 abstract "Existing one-shot face reenactment methods either present obvious artifacts in large pose transformations, or cannot well-preserve the identity information in the source images, or fail to meet the requirements of real-time applications due to the intensive amount of computation involved. In this paper, we introduce $$text {Face2Face}^rho $$ , the first Real-time High-resolution and One-shot (RHO, $$rho $$ ) face reenactment framework. To achieve this goal, we designed a new 3DMM-assisted warping-based face reenactment architecture which consists of two fast and efficient sub-networks, i.e., a u-shaped rendering network to reenact faces driven by head poses and facial motion fields, and a hierarchical coarse-to-fine motion network to predict facial motion fields guided by different scales of landmark images. Compared with existing state-of-the-art works, $$text {Face2Face}^rho $$ can produce results of equal or better visual quality, yet with significantly less time and memory overhead. We also demonstrate that $$text {Face2Face}^rho $$ can achieve real-time performance for face images of $$1440times 1440$$ resolution with a desktop GPU and $$256times 256$$ resolution with a mobile CPU." @default.
- W4312365317 created "2023-01-04" @default.
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- W4312365317 date "2022-01-01" @default.
- W4312365317 modified "2023-10-14" @default.
- W4312365317 title "$$text {Face2Face}^rho $$: Real-Time High-Resolution One-Shot Face Reenactment" @default.
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- W4312365317 doi "https://doi.org/10.1007/978-3-031-19778-9_4" @default.
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