Comments (3)
Hi!
I read paper very detail and have some questions to ask.
The first question is that may I know the detail about fitting process with adam slover?
I know that the model fitting is not cnn-based but optimization-based, and i want to know how fitting process do.
I'm appreciate if you could share some hints with me.
The second question is about I want to check the training data of texture GAN model.
When training PGGAN, the training data is 10,000 UV maps(from raw mesh data) right?
And how can I decide the input vector of texture generator?
Can we just input the random vector to optimize or others?
I'm really interested in your work and want to know much more details.
Thank you very much!
from ganfit.
Sorry, we forgot that detail in the paper. As far as I remember, it was 1-2 weeks on dual 1080TI
from ganfit.
Hey @vitahsu ,
Thank you for your interest in our work. Below are the answers to your questions:
1- The fitting is actually quite similar to the way we train a CNN. But this time, we update latent parameters rather than the network parameters and keep the texture network and shape models fixed. You need to specify to your optimizer that which parameters you want to optimize, in our case, that is latent parameters of shape and texture models as well as camera and illumination parameters (as explained in the paper). Let me know if you have more specific question regarding this.
2.a- 10,000 UV maps are not from raw mesh, the scans are registered and textures are transfered to a common UV template, which means face parts corresponds to the same regions in the UV map for all textures. But yes, the training data consist of UV images.
2.b- You don't need to decide the input vector, it is randomly given during PGGAN training. You may also use something more meaningful but we haven't done such.
2.c- You can initialize randomly at the begining of the optimization and it should be just fine. However, if you have a look to our journal version, FastGANFit, it is often more stable to have an initializer CNN network that gives you initial estimates of the latent parameters.
Sorry for answering you so late. Hope this information is still useful. Let me know if you have any other questions. Cheers!
from ganfit.
Related Issues (20)
- Amazing work! Would love to try out the demos! HOT 1
- Will the code be public? HOT 1
- Texture coordinate HOT 1
- About How to iteratively update the input vector Pt of PGGAN HOT 3
- How to evaluate florence dataset for my model HOT 1
- About images of MOFA-test dataset HOT 1
- How to get the ground truth and template landmarks HOT 2
- A problem about paper HOT 3
- How to send you images? HOT 1
- ValueError: The glob ganfit_plus/subject_01/Model/frontal1/obj/*.obj yields no assets HOT 1
- Which UV texture dataset did you use to train proGAN? HOT 1
- 没有开源代码放上来干嘛,又当又立么 HOT 2
- The uv format of GANFIT
- latent parameters HOT 1
- When can authors share source code and trained models? HOT 4
- Input of the Texture GAN HOT 4
- About the dataset HOT 6
- unable to reproduce the result HOT 1
- About the Texture GAN HOT 17
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 ganfit.