One has to take into consideration the geometry, the reflectance properties, pose, and the illumination of both faces, and make sure that mouth. Moreover, it outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436) images. In computer animation, animating human faces is an art itself, but transferring expressions from one human to someone else is an even more complex task. PWC-Net is 17 times smaller in size and easier to train than the recent FlowNet2 model. It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow. the download link below to get the latest version from the GitHub repository.
#FACE2FACE GITHUB INSTALL#
CUDA 10.2 or above Python 3.8.5 pip install -r requirements.txt. Cast in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow estimate to warp the CNN features of the second image. Cambridge Face2Face Second Edition (PDF, 3rd edition Paperback 1 October. Face2Face : Official Pytorch Implementation Environment. PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume. We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. Deqing Sun, Xiaodong Yang, Ming-Yu Liu, Jan Kautz
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