Wywu.github.io receives about 399 visitors in one month. That could possibly earn $2 each month or $0.07 each day. Server of the website is located in the United States. Wywu.github.io main page was reached and loaded in 0.42 seconds. This is a good result. Try the services listed at the bottom of the page to search for available improvements.
Is wywu.github.io legit? | |
Website Value | $36 |
Alexa Rank | 9042409 |
Monthly Visits | 399 |
Daily Visits | 14 |
Monthly Earnings | $2 |
Daily Earnings | $0.07 |
Country: United States
Metropolitan Area: Not defined
Postal Reference Code: Not defined
Latitude: 37.751
Longitude: -97.822
HTML Tag | Content | Informative? |
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Title: | Wayne Wu's Homepage - CS at Tsinghua | Could be improved |
Description: | Not set | Empty |
H1: | Wayne Wu | Is it informative enough? |
H2: | 吳文巖 | Is it informative enough? |
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/./projects/LAB/WFLW.html: | |
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Title |
Look at Boundary: A Boundary-Aware Face Alignment Algorithm |
Description |
Wider Facial Landmarks in-the-wild (WFLW) contains 10000 faces (7500 for training and 2500 for testing) with 98 fully manual annotated landmarks. Apart from landmark annotation, out new dataset includes rich attribute annotations, i.e., occlusion, pose, make-up, illumination, blur and expression for comprehensive ysis of existing algorithms. Compare to previous dataset, faces in the proposed dataset introduce large variations in expression, pose and occlusion. We can simply evaluate the robustness of pose, occlusion, and expression on proposed dataset instead of switching between multiple evaluation protocols in different datasets. [censored]
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Look at Boundary: A Boundary-Aware Face Alignment Algorithm |
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/./projects/ReenactGAN/ReenactGAN.html: | |
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Title |
ReenactGAN: Learning to Reenact Faces via Boundary Transfer |
Description |
We present a novel learning-based framework for face reenactment. The proposed method, known as ReenactGAN, is capable of transferring facial movements and expressions from an arbitrary person’s monocular video input to a target person’s video. Instead of performing a direct transfer in the pixel space, which could result in structural artifacts, we first map the source face onto a boundary latent space. A transformer is subsequently used to adapt the source face’s boundary to the target’s boundary. Finally, a target-specific decoder is used to generate the reenacted target face. Thanks to the effective and reliable boundary-based transfer, our method can perform photo-realistic face reenactment. In addition, ReenactGAN is appealing in that the whole reenactment process is purely feed-forward, and thus the reenactment process can run in real-time (30 FPS on one GTX 1080 GPU). |
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ReenactGAN: Learning to Reenact Faces via Boundary Transfer |
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/./projects/LAB/LAB.html: | |
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Title |
Look at Boundary: A Boundary-Aware Face Alignment Algorithm |
Description |
We present a novel boundary-aware face alignment algorithm by utilising boundary lines as the geometric structure of a human face to help facial landmark localisation. Unlike the conventional heatmap based method and regression based method, our approach derives face landmarks from boundary lines which remove the ambiguities in the landmark definition. Three questions are explored and answered by this work: 1. Why using boundary? 2. How to use boundary? 3. What is the relationship between boundary estimation and landmarks localisation? Our boundary-aware face alignment algorithm achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin. Our method can also easily integrate information from other datasets. By utilising boundary information of 300-W dataset, our method achieves 3.92% mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training and testing across different factors, including poses, expressions, illuminations, makeups, occlusions, and blurriness. |
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Look at Boundary: A Boundary-Aware Face Alignment Algorithm |
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You took 89.95 and 84.95 at the same time from my back account that i didnt authorize and was apparently hacked. I...