392257 ISY Project: Deep Face Editing with StyleGAN (Pj) (SoSe 2021)
Contents, comment
-
In the project, you will gain practical experience with style generative adversarial network (StyleGAN) [see References], apply neural networks, and computational approaches to produce faces and objects transformations.
References:
This Person Does Not Exist https://thispersondoesnotexist.com
Animate your family photos https://www.myheritage.com/deep-nostalgia
Microsoft Face Demo https://azure.microsoft.com/en-us/services/cognitive-services/face
AI Makes Near-Perfect DeepFakes in 40 Seconds! https://www.youtube.com/watch?v=iXqLTJFTUGc
Two Minute Papers: 3 New Things An AI Can Do With Your Photos! https://youtu.be/B8RMUSmIGCI
Two Minute Papers: Everybody Can Make Deepfakes Now! https://youtu.be/mUfJOQKdtAk
Arxiv Insights: Face editing with Generative Adversarial Networks https://youtu.be/dCKbRCUyop8
Henry AI Labs: StyleGAN https://youtu.be/AQBti_wN414
NVidia just released StyleGAN 2 https://youtu.be/BIZg_PPuj_0
When A.I. Becomes Creative https://youtu.be/KZ7BnJb30Cc
GitHub: OpenFace https://github.com/TadasBaltrusaitis/OpenFace
GitHub: DeepFaceLab https://github.com/iperov/DeepFaceLab
GitHub: Neural Photo Editor https://github.com/ajbrock/Neural-Photo-Editor
GitHub: Stylegan2encoder https://github.com/robertluxemburg/stylegan2encoder
GitHub: Official code for StyleGAN2 https://github.com/NVlabs/stylegan2
Requirements for participation, required level
-
- Introduction to Neural Networks or Advanced Neural Networks courses.
- Python or C++ ( > 1 year).
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period |
|---|
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-M-Inf-GP Grundlagenprojekt Intelligente Systeme | Gruppenprojekt | Ungraded examination
|
Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
- Registered number: 3
- This is the number of students having stored the course in their timetable. In brackets, you see the number of users registered via guest accounts.
- Address:
- SS2021_392257@ekvv.uni-bielefeld.de
- This address can be used by teaching staff, their secretary's offices as well as the individuals in charge of course data maintenance to send emails to the course participants. IMPORTANT: All sent emails must be activated. Wait for the activation email and follow the instructions given there.
- If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_262906392@ekvv.uni-bielefeld.de
- Coverage:
- 3 Students to be reached directly via email
- Notes:
- Additional notes on the electronic mailing lists
- Last update basic details/teaching staff:
- Thursday, February 4, 2021
- Last update times:
- Thursday, February 4, 2021
- Last update rooms:
- Thursday, February 4, 2021
- Type(s) / SWS (hours per week per semester)
- project (Pj) / 4
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
- Questions or corrections?
- Questions or correction requests for this course?
- Planning support
- Clashing dates for this course
- Links to this course
- If you want to set links to this course page, please use one of the following links. Do not use the link shown in your browser!
- The following link includes the course ID and is always unique:
- https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=262906392
- Send page to mobile
-
Click to open QR code
Scan QR code:
- ID
- 262906392