Comments (3)
This paper obviously involves a lot of similar techniques to the one used in faceswap. The big difference is that their model is unidirectional and makes assumptions about pose that allow it to run faster. These assumptions would not work in our context since we want a variety of poses (The face pointing different directions in the final video). It's possible that /u/deepfakes actually read this paper in designing his model.
Many of the methods in the paper are in use in the model used in faceswaps. It already treats lighting as a discriminator in the face matching. The loss function is not terribly different from faceswap, though the weights are different, the main features which are calculated are very similar.
I am not certain whether the model in this paper would be faster or better for us, even if we allowed for pose differentiation, the model is unidirectional. The unidirectional nature of the model may allow it to be faster, since only one set of matrix transformations are necessary in training, but may make the model less effective an facial transforms. Changing faceswap to use the model in the paper would require a fair amount of work since the current system assumes bidirectionality.
In the end, this paper seems interesting, but probably not helpful as it's innovations are antithetical to our use cases.
from faceswap-model.
This paper was published before cycleGAN and UNIT. It's sad that even a 1.5-year-old paper seems obsolete (hyperbole here) in deep learning era.
Nevertheless it provides many insights/techniques for face swapping, e.g., pyramidal (multi-scale) architecture, light. loss, showing effectiveness of style transfer approach on human face, and U-NET for post-processing segmentation. I think the segmentation model can be applied to replace the current landmarks convex hull. See how it handles hairs/bangs in Fig. 5. But unfortunately there is not much information about how the segmentation network was trained.
from faceswap-model.
Yeah, I saw that, but it seems that for the final picture they're just cutting the replacement face off RIGHT above the eyes. So the forehead is getting no treatment at all. In fact, they actually mention that their model changes "nose, eyes, eyebrows, lips and facial wrinkles" and you can see how those details are really the only ones changed. I think, in general, faceswap is significantly more advanced and that this paper is less than useful in advancing the models we have.
from faceswap-model.
Related Issues (20)
- [Google Research Blog] Mobile Real-time Video Segmentation
- GitHub repos for 3D Face Model
- Resources on faces/poses generative network HOT 1
- Resources for image refinment HOT 2
- Learning rate, beta1, beta 2 in adam optimizer in models HOT 11
- neural enchance to improve resolution HOT 1
- Survey of Model Improvements Proposals HOT 1
- [Featured] Deepfakes uses in the wild HOT 2
- [Paper] Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network HOT 5
- Paper & source: progressive growing of gans HOT 4
- Why do two autoencoders need to be trained? HOT 2
- Histogramm loss func.
- How do I reuse models in other faceswaps? HOT 4
- Does anyone successfully train a generic encoder?
- Paper : Boundary-Aware Face Alignment Algorithm
- How does the loss minimize if autoencoder_B keeps changing the weights learned by autoencoder_A ? HOT 2
- NVIDIA GAN for higly detailed output
- Why do we use warped images as part of the loss function?
- can anyone tell me what is a masked model ? HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from faceswap-model.