Marco Marchesi - Practical uses of style transfer in the creative industry

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This talk by Marco Marchesi from Happy Finish on "Can you make this image more neoclassical? Practical uses of Style Transfer in the creative industry" was presented at the Style Transfer event on 18th April at TechHub as part of the Creative AI meetup.

Tecnologia

CREATIVE AI MEETUP - 18.04.18
"Can you make this image more neoclassical?"
Practical uses of Style Transfer in the creative industry
Marco Marchesi
Head of Technology
Happy Finish

CREATIVE AI MEETUP - 18.04.18
Summary
● So far…
● Yinka Shonibare VR
● Other approaches: GAN’s
● Deep Retouching
● This year
● Conclusions
from “Come Swim” (2017)

CREATIVE AI MEETUP - 18.04.18
So far...
Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015).
https://harishnarayanan.org/writing/artistic-style-transfer

CREATIVE AI MEETUP - 18.04.18
So far...
Jing, Yongcheng, et al. "Neural style transfer: A review." arXiv preprint arXiv:1705.04058 (2018). https://github.com/ycjing/Neural-Style-Transfer-Papers

CREATIVE AI MEETUP - 18.04.18
Royal Academy - Yinka Shonibare VR
“The Townley Venus”
(1-2 century AD)
Shonibare’s
“Townley Venus”
(2017)
Gavin Hamilton’s “Venus
giving Helen to Paris as his
wife” (1782-1784)

CREATIVE AI MEETUP - 18.04.18
Royal Academy - Yinka Shonibare VR

CREATIVE AI MEETUP - 18.04.18
Texture Synthesis
Wilmot, Pierre, Eric Risser, and Connelly Barnes. "Stable and controllable neural texture synthesis and style transfer using histogram losses." arXiv preprint
arXiv:1701.08893 (2017). https://github.com/ycjing/Neural-Style-Transfer-Papers

CREATIVE AI MEETUP - 18.04.18
First Tests
Test 1 Test 2 Test 3
Style Mask
Mask Content

CREATIVE AI MEETUP - 18.04.18
Image Analogy...
Champandard, Alex J. "Semantic style transfer and turning two-bit doodles into fine artworks." arXiv preprint arXiv:1603.01768
(2016).https://github.com/DmitryUlyanov/fast-neural-doodle
Liao, Jing, et al. "Visual attribute transfer through deep image analogy." arXiv preprint arXiv:1705.01088 (2017).
https://github.com/awentzonline/image-analogies

CREATIVE AI MEETUP - 18.04.18
...that actually makes art!

CREATIVE AI MEETUP - 18.04.18
Our Process
CONTENT
TEXTURE IMAGE
STYLE
IMAGE
CONTENT
MASK
STYLE
MASK
NEURAL STYLE TRANSFER
TARGET
IMAGE
CONTENT
MASK
TARGET
TEXTURE IMAGE
video

CREATIVE AI MEETUP - 18.04.18
Final Result
video

CREATIVE AI MEETUP - 18.04.18
Another Approach: Generative Adversarial Networks
Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." arXiv preprint arXiv:1703.10593 (2017).
https://github.com/junyanz/CycleGAN
Huang, Xun, et al. “Multimodal Unsupervised Image-to-Image Translation”. arXiv preprint arXiv:1804.04732 (2018)) https://github.com/NVlabs/MUNIT
CycleGAN (2017) MUNIT (2018)

CREATIVE AI MEETUP - 18.04.18
Deep Retouching (Happy Finish, 2017)
Patent Pending

CREATIVE AI MEETUP - 18.04.18
This year: Deep Painterly Harmonization
(Cornell U., Adobe, 2018)
Luan, Fujun, et al. "Deep Painterly Harmonization." arXiv preprint arXiv:1804.03189 (2018). https://github.com/luanfujun/deep-painterly-harmonization

CREATIVE AI MEETUP - 18.04.18
This year: Fast Photo Style (NVIDIA, 2018)
Li, Yijun, et al. "A Closed-form Solution to Photorealistic Image Stylization." arXiv preprint arXiv:1802.06474 (2018). https://github.com/NVIDIA/FastPhotoStyle

CREATIVE AI MEETUP - 18.04.18
The semantic limitations of style transfer

CREATIVE AI MEETUP - 18.04.18
This year: Photo Style Transfer (theprintspace, 2018)
Patent Pending
ContentStyle Cornell U., Adobe 2017 NVIDIA 2018 theprintspace 2018

CREATIVE AI MEETUP - 18.04.18
Conclusions
Patent Pending
● Semantically consistent results
● Really Hi-Res
● More content transformation
What’s next?

www.happyfinish.com Everything is possible.
Marco Marchesi| Head of Technology
marco.marchesi@happyfinish.com

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Presented live at FITC Toronto 2018 More info at http://fitc.ca/event/to18/ Presented by Xavier Snelgrove, Element AI Overview The last couple years have seen an explosion in research using AI and machine learning techniques for creative purposes, especially for image creation. If you’ve seen apps like Prisma, or images from Google’s Deep Dream, then you’ve seen these algorithms in action. It’s early days, and while some of the new aesthetics are beautiful, the tools and approaches are still embryonic. In this talk Xavier will show work he and others have made with new algorithms, and explain at a high level how some of these neural-network based tools actually work. In particular he’ll explain his “multiscale neural texture synthesis” algorithm that has allowed for the creation of much more varied and high-resolution images. Many of these techniques are not yet usable by people without a deep technical background, and Xavier will discuss directions he is taking his work to try to make the algorithms more accessible and expressive. Just as you don’t need to be an audio engineer to create electronic music on a synthesizer, neither should you need to be an AI researcher to create electronic images with a neural net. Just as you don’t expect a synthesizer to write your music, neither should we look to algorithms to create our images. Objective To share some of the advances in using AI to create images, and explain how the algorithms work. Target Audience Technically curious creatives who want to better understand how visual neural algorithms like style transfer and deep dream actually work. Assumed Audience Knowledge None: the talk will be mostly accessible, with just occasional more hardcore diversions Five Things Audience Members Will Learn How a neural network works (at a very high level) How style transfer algorithms (like Prisma) work Some tools they can use today to create neural images Current frontiers in creative AI Interesting artists working with AI today

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Marco Marchesi - Practical uses of style transfer in the creative industry

1. CREATIVE AI MEETUP - 18.04.18 "Can you make this image more neoclassical?" Practical uses of Style Transfer in the creative industry Marco Marchesi Head of Technology Happy Finish

2. CREATIVE AI MEETUP - 18.04.18 Summary ● So far… ● Yinka Shonibare VR ● Other approaches: GAN’s ● Deep Retouching ● This year ● Conclusions from “Come Swim” (2017)

3. CREATIVE AI MEETUP - 18.04.18 So far... Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015). https://harishnarayanan.org/writing/artistic-style-transfer

4. CREATIVE AI MEETUP - 18.04.18 So far... Jing, Yongcheng, et al. "Neural style transfer: A review." arXiv preprint arXiv:1705.04058 (2018). https://github.com/ycjing/Neural-Style-Transfer-Papers

5. CREATIVE AI MEETUP - 18.04.18 Royal Academy - Yinka Shonibare VR “The Townley Venus” (1-2 century AD) Shonibare’s “Townley Venus” (2017) Gavin Hamilton’s “Venus giving Helen to Paris as his wife” (1782-1784)

6. CREATIVE AI MEETUP - 18.04.18 Royal Academy - Yinka Shonibare VR

7. CREATIVE AI MEETUP - 18.04.18 Texture Synthesis Wilmot, Pierre, Eric Risser, and Connelly Barnes. "Stable and controllable neural texture synthesis and style transfer using histogram losses." arXiv preprint arXiv:1701.08893 (2017). https://github.com/ycjing/Neural-Style-Transfer-Papers

8. CREATIVE AI MEETUP - 18.04.18 First Tests Test 1 Test 2 Test 3 Style Mask Mask Content

9. CREATIVE AI MEETUP - 18.04.18 Image Analogy... Champandard, Alex J. "Semantic style transfer and turning two-bit doodles into fine artworks." arXiv preprint arXiv:1603.01768 (2016).https://github.com/DmitryUlyanov/fast-neural-doodle Liao, Jing, et al. "Visual attribute transfer through deep image analogy." arXiv preprint arXiv:1705.01088 (2017). https://github.com/awentzonline/image-analogies

10. CREATIVE AI MEETUP - 18.04.18 ...that actually makes art!

11. CREATIVE AI MEETUP - 18.04.18 Our Process CONTENT TEXTURE IMAGE STYLE IMAGE CONTENT MASK STYLE MASK NEURAL STYLE TRANSFER TARGET IMAGE CONTENT MASK TARGET TEXTURE IMAGE video

12. CREATIVE AI MEETUP - 18.04.18 Final Result video

13. CREATIVE AI MEETUP - 18.04.18 Another Approach: Generative Adversarial Networks Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." arXiv preprint arXiv:1703.10593 (2017). https://github.com/junyanz/CycleGAN Huang, Xun, et al. “Multimodal Unsupervised Image-to-Image Translation”. arXiv preprint arXiv:1804.04732 (2018)) https://github.com/NVlabs/MUNIT CycleGAN (2017) MUNIT (2018)

14. CREATIVE AI MEETUP - 18.04.18 Deep Retouching (Happy Finish, 2017) Patent Pending

15. CREATIVE AI MEETUP - 18.04.18 Deep Retouching (Happy Finish, 2017) Patent Pending

16. CREATIVE AI MEETUP - 18.04.18 Deep Retouching (Happy Finish, 2017) Patent Pending

17. CREATIVE AI MEETUP - 18.04.18 This year: Deep Painterly Harmonization (Cornell U., Adobe, 2018) Luan, Fujun, et al. "Deep Painterly Harmonization." arXiv preprint arXiv:1804.03189 (2018). https://github.com/luanfujun/deep-painterly-harmonization

18. CREATIVE AI MEETUP - 18.04.18 This year: Fast Photo Style (NVIDIA, 2018) Li, Yijun, et al. "A Closed-form Solution to Photorealistic Image Stylization." arXiv preprint arXiv:1802.06474 (2018). https://github.com/NVIDIA/FastPhotoStyle

19. CREATIVE AI MEETUP - 18.04.18 The semantic limitations of style transfer

20. CREATIVE AI MEETUP - 18.04.18 This year: Photo Style Transfer (theprintspace, 2018) Patent Pending ContentStyle Cornell U., Adobe 2017 NVIDIA 2018 theprintspace 2018

21. CREATIVE AI MEETUP - 18.04.18 This year: Photo Style Transfer (theprintspace, 2018) Patent Pending ContentStyle Cornell U., Adobe 2017 NVIDIA 2018 theprintspace 2018

22. CREATIVE AI MEETUP - 18.04.18 Conclusions Patent Pending ● Semantically consistent results ● Really Hi-Res ● More content transformation What’s next?

23. www.happyfinish.com Everything is possible. Marco Marchesi| Head of Technology marco.marchesi@happyfinish.com

Marco Marchesi - Practical uses of style transfer in the creative industry

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