2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-5.12
Paper Title Pose-Guided and Style-Transferred Face Reenactment
Authors Gee-Sern Jison Hsu, Hung-Yi Wu, National Taiwan University of Science and Technology, Taiwan
SessionARS-5: Image and Video Synthesis, Rendering and Visualization
LocationArea I
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Synthesis, Rendering, and Visualization
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Unlike most face reenactment that deals with minor pose variation, we propose the Pose-Guided Reenactment (PGR) framework for large-pose face reenactment. The proposed PGR is composed of a landmark detector, a landmark encoder, a face encoder, a landmark decoder, a style encoder, a face generator and two discriminators. The training of these components is divided into two phases. In Phase I, the landmark encoder is trained to encode the landmarks of the reference face to a reference landmark code, and the face encoder is trained to encode the source face to a source face code. The reference landmark code and the source face code are entered to the landmark decoder, which generates a target landmark set. In Phase II, the face generator is trained to generate the target face with the desired identity, pose and expression, given the target landmark set and the source face as input. To handle large pose, we include the large pose data in the training set and propose the pose-guided landmark switch to control the change of the facial landmarks during pose variation. Experiments on MPIE and VoxCeleb1 benchmark databases show that the proposed PGR can effectively reenact the faces with large pose, and delivers a state-of-the-art overall performance compared with other approaches.