ICCVi EVENT TLV19

ALL PRESENTATIONS FROM THE ICCVi EVENT TLV19

1909.05379


  • Alon Shoshan, Roey Mechrez, Lihi Zelnik-Manor: Dynamic-Net: Tuning the Objective Without Re-Training for Synthesis Tasks.
    GitHub arXiv
1811.08760

Dynamic_Net_supplementary_ICCV


  • Yuval Nirkin, Yosi Keller, Tal Hassner: FSGAN: Subject Agnostic Face Swapping and Reenactment.
    Medium ODSC GroundAI arXiv
1908.05932


  • Nadav Schor, Oren Katzir, Hao Zhang, Daniel Cohen-Or: CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition.
    arXiv
1811.07441


1811.10203v3-1


1909.01595v1


Live-Face-De-Identification-in-Video


1904.00415v2


1907.11565


1812.00231


1908.11628v1


1811.08126


1903.08889


1908.01373


Best Paper Award (Marr Prize): SinGAN: Learning a Generative Model from a Single Natural Image

[pdf] [supp] [bibtex]

𝗦𝗶𝗻𝗚𝗔𝗡: 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹 𝗳𝗿𝗼𝗺 𝗮 𝗦𝗶𝗻𝗴𝗹𝗲 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗜𝗺𝗮𝗴𝗲• • •𝗝𝗼𝗶𝗻 𝘂𝘀 𝗮𝗻𝗱 𝗹𝗲𝘁’𝘀 𝗱𝗲𝘀𝗶𝗴𝗻 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱, 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿.

𝘞𝘦 𝘪𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦 𝘚𝘪𝘯𝘎𝘈𝘕, 𝘢𝘯 𝘶𝘯𝘤𝘰𝘯𝘥𝘪𝘵𝘪𝘰𝘯𝘢𝘭 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘮𝘰𝘥𝘦𝘭 𝘵𝘩𝘢𝘵 𝘤𝘢𝘯 𝘣𝘦 𝘭𝘦𝘢𝘳𝘯𝘦𝘥 𝘧𝘳𝘰𝘮 𝘢 𝘴𝘪𝘯𝘨𝘭𝘦 𝘯𝘢𝘵𝘶𝘳𝘢𝘭 𝘪𝘮𝘢𝘨𝘦. 𝘖𝘶𝘳 𝘮𝘰𝘥𝘦𝘭 𝘪𝘴 𝘵𝘳𝘢𝘪𝘯𝘦𝘥 𝘵𝘰 𝘤𝘢𝘱𝘵𝘶𝘳𝘦 𝘵𝘩𝘦 𝘪𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯 𝘰𝘧 𝘱𝘢𝘵𝘤𝘩𝘦𝘴 𝘸𝘪𝘵𝘩𝘪𝘯 𝘵𝘩𝘦 𝘪𝘮𝘢𝘨𝘦, 𝘢𝘯𝘥 𝘪𝘴 𝘵𝘩𝘦𝘯 𝘢𝘣𝘭𝘦 𝘵𝘰 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦 𝘩𝘪𝘨𝘩 𝘲𝘶𝘢𝘭𝘪𝘵𝘺, 𝘥𝘪𝘷𝘦𝘳𝘴𝘦 𝘴𝘢𝘮𝘱𝘭𝘦𝘴 𝘵𝘩𝘢𝘵 𝘤𝘢𝘳𝘳𝘺 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘷𝘪𝘴𝘶𝘢𝘭 𝘤𝘰𝘯𝘵𝘦𝘯𝘵 𝘢𝘴 𝘵𝘩𝘦 𝘪𝘮𝘢𝘨𝘦. 𝘚𝘪𝘯𝘎𝘈𝘕 𝘤𝘰𝘯𝘵𝘢𝘪𝘯𝘴 𝘢 𝘱𝘺𝘳𝘢𝘮𝘪𝘥 𝘰𝘧 𝘧𝘶𝘭𝘭𝘺 𝘤𝘰𝘯𝘷𝘰𝘭𝘶𝘵𝘪𝘰𝘯𝘢𝘭 𝘎𝘈𝘕𝘴, 𝘦𝘢𝘤𝘩 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘪𝘣𝘭𝘦 𝘧𝘰𝘳 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘵𝘩𝘦 𝘱𝘢𝘵𝘤𝘩 𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯 𝘢𝘵 𝘢 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘴𝘤𝘢𝘭𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘪𝘮𝘢𝘨𝘦. 𝘛𝘩𝘪𝘴 𝘢𝘭𝘭𝘰𝘸𝘴 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘯𝘨 𝘯𝘦𝘸 𝘴𝘢𝘮𝘱𝘭𝘦𝘴 𝘰𝘧 𝘢𝘳𝘣𝘪𝘵𝘳𝘢𝘳𝘺 𝘴𝘪𝘻𝘦 𝘢𝘯𝘥 𝘢𝘴𝘱𝘦𝘤𝘵 𝘳𝘢𝘵𝘪𝘰, 𝘵𝘩𝘢𝘵 𝘩𝘢𝘷𝘦 𝘴𝘪𝘨𝘯𝘪𝘧𝘪𝘤𝘢𝘯𝘵 𝘷𝘢𝘳𝘪𝘢𝘣𝘪𝘭𝘪𝘵𝘺, 𝘺𝘦𝘵 𝘮𝘢𝘪𝘯𝘵𝘢𝘪𝘯 𝘣𝘰𝘵𝘩 𝘵𝘩𝘦 𝘨𝘭𝘰𝘣𝘢𝘭 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘧𝘪𝘯𝘦 𝘵𝘦𝘹𝘵𝘶𝘳𝘦𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘪𝘮𝘢𝘨𝘦. 𝘐𝘯 𝘤𝘰𝘯𝘵𝘳𝘢𝘴𝘵 𝘵𝘰 𝘱𝘳𝘦𝘷𝘪𝘰𝘶𝘴 𝘴𝘪𝘯𝘨𝘭𝘦 𝘪𝘮𝘢𝘨𝘦 𝘎𝘈𝘕 𝘴𝘤𝘩𝘦𝘮𝘦𝘴, 𝘰𝘶𝘳 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘪𝘴 𝘯𝘰𝘵 𝘭𝘪𝘮𝘪𝘵𝘦𝘥 𝘵𝘰 𝘵𝘦𝘹𝘵𝘶𝘳𝘦 𝘪𝘮𝘢𝘨𝘦𝘴, 𝘢𝘯𝘥 𝘪𝘴 𝘯𝘰𝘵 𝘤𝘰𝘯𝘥𝘪𝘵𝘪𝘰𝘯𝘢𝘭 (𝘪.𝘦. 𝘪𝘵 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦𝘴 𝘴𝘢𝘮𝘱𝘭𝘦𝘴 𝘧𝘳𝘰𝘮 𝘯𝘰𝘪𝘴𝘦). 𝘜𝘴𝘦𝘳 𝘴𝘵𝘶𝘥𝘪𝘦𝘴 𝘤𝘰𝘯𝘧𝘪𝘳𝘮 𝘵𝘩𝘢𝘵 𝘵𝘩𝘦 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦𝘥 𝘴𝘢𝘮𝘱𝘭𝘦𝘴 𝘢𝘳𝘦 𝘤𝘰𝘮𝘮𝘰𝘯𝘭𝘺 𝘤𝘰𝘯𝘧𝘶𝘴𝘦𝘥 𝘵𝘰 𝘣𝘦 𝘳𝘦𝘢𝘭 𝘪𝘮𝘢𝘨𝘦𝘴. 𝘞𝘦 𝘪𝘭𝘭𝘶𝘴𝘵𝘳𝘢𝘵𝘦 𝘵𝘩𝘦 𝘶𝘵𝘪𝘭𝘪𝘵𝘺 𝘰𝘧 𝘚𝘪𝘯𝘎𝘈𝘕 𝘪𝘯 𝘢 𝘸𝘪𝘥𝘦 𝘳𝘢𝘯𝘨𝘦 𝘰𝘧 𝘪𝘮𝘢𝘨𝘦 𝘮𝘢𝘯𝘪𝘱𝘶𝘭𝘢𝘵𝘪𝘰𝘯 𝘵𝘢𝘴𝘬𝘴.
𝙏𝙖𝙢𝙖𝙧 𝙍𝙤𝙩𝙩 𝙎𝙝𝙖𝙝𝙖𝙢, 𝙏𝙖𝙡𝙞 𝘿𝙚𝙠𝙚𝙡, 𝙏𝙤𝙢𝙚𝙧 𝙈𝙞𝙘𝙝𝙖𝙚𝙡𝙞
https://tomer.net.technion.ac.il/
https://www.linkedin.com/in/tomermichaeli/
https://www.linkedin.com/in/tali-dekel-1975b338/
https://www.linkedin.com/in/tamar-rott-shaham-b17714162/
• • •
https://codesign.blog/event/iccv-2019-seoul-korea/
https://codesign.blog/2019/10/06/all-presentations-from-the-iccvi-event-tlv19/
https://codesign.blog/2019/11/16/win-ai-2019/

• • •
CVF Open Access>> http://openaccess.thecvf.com/content_ICCV_2019/papers/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.pdf
arXiv>> https://arxiv.org/abs/1905.01164
PDF>> https://arxiv.org/pdf/1905.01164.pdf
Medium>> https://medium.com/syncedreview/iccv-2019-best-papers-announced-27a1a21311e1
Quora>> https://www.quora.com/q/deeplearning/SinGAN-Learning-a-Generative-Model-from-a-Single-Natural-Image
GitHub>> https://github.com/FriedRonaldo/SinGAN
Tamar Rott Shaham’s GitHub>> https://github.com/tamarott/SinGAN
reddit>> https://www.reddit.com/r/MachineLearning/comments/doiukh/190501164_singan_learning_a_generative_model_from/

• • •

Google at ICCV 2019>> https://ai.googleblog.com/2019/10/google-at-iccv-2019.html
Semantic Scholar>> https://www.semanticscholar.org/paper/SinGAN%3A-Learning-a-Generative-Model-from-a-Single-Shaham-Dekel/ccaf15d4ad006171061508ca0a99c73814671501
CVF Open Access>> http://openaccess.thecvf.com/content_ICCV_2019/html/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.html
ResearchGate>> https://www.researchgate.net/publication/332873614_SinGAN_Learning_a_Generative_Model_from_a_Single_Natural_Image
“ICCV 2019 Review [2] Best Paper SinGAN: Learning a Generative Model from a Single Natural Image 리뷰”

• • •

𝗝𝗼𝗶𝗻 𝘂𝘀 𝗮𝗻𝗱 𝗹𝗲𝘁’𝘀 𝗱𝗲𝘀𝗶𝗴𝗻 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱, 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿.
https://codesign.blog/community

SinGAN-Learning-a-Generative-Model-from-a-Single-Natural-Image


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