Generative Adversarial Networks for Anime Character Generation
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The document discusses recent advances in generative adversarial networks (GANs) for image generation. It summarizes two influential GAN models: ProgressiveGAN (Karras et al., 2018) and BigGAN (Brock et al., 2019). ProgressiveGAN introduced progressive growing of GANs to produce high resolution images. BigGAN scaled up GAN training through techniques like large batch sizes and regularization methods to generate high fidelity natural images. The document also discusses using GANs to generate full-body, high-resolution anime characters and adding motion through structure-conditional GANs.
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Generative Adversarial Networks for Anime Character Generation
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- 24. #denatechcon
- 25. #denatechcon 1 3 5 7 2 4 6 8
- 26. #denatechcon 1 3 5 7 2 4 6 8
- 27. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019) 1 3 5 7 2 4 6 8
- 28. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
- 29. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
- 30. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
- 31. #denatechcon
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- 33. #denatechcon
- 34. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
- 35. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
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- 40. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
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- 44. #denatechcon Large Scale GAN Training for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019. (512x512)
- 45. #denatechcon Large Scale GAN Training for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019. (512x512)
- 46. #denatechcon Large Scale GAN Training for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019. (512x512)
- 47. #denatechcon (512x512) Large Scale GAN Training for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
- 48. #denatechcon (512x512) Large Scale GAN Training for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
- 49. #denatechcon Large Scale GAN Training for High Fidelity Natural Image Synthesis. Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019. (512x512)
- 50. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
- 51. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
- 52. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
- 53. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
- 54. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. 00./ 0
- 55. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. 00./ 0
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- 67. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
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- 69. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. 00./ 0
- 70. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. 00./ 0
- 71. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. 0 0 0 0 0 0 0 0 0 0 0
- 72. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018.
- 73. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018.
- 74. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. 0 0 0 0 0 0 0 0 0 0 0
- 75. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018.
- 76. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018.
- 77. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018.
- 78. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. 00./ 0
- 79. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. 00./ 0
- 80. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. 00./ 0 Full-body anime generation at 1024x1024 with Progressive Structure-conditional GANs
- 81. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. 00./ 0
- 82. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. // . 0/0 Adding action to full-body anime characters with Progressive Structure-conditional GANs
- 83. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018.
- 84. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. (ICLR’18)
- 85. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. (ICLR’18)
- 86. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. (ICLR’18) (NIPS’17) (NIPS’17)
- 87. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018. (ICLR’18) (NIPS’17) (NIPS’17)
- 88. #denatechcon Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, and Yusuke Uchida. In ECCVW 2018.
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- 115. #denatechcon /30 480 6 2/81 4C + 60 2 • 8 , Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation. Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz. In CVPR 2018. https://youtu.be/MjViy6kyiqs Research at NVIDIA: Transforming Standard Video Into Slow Motion with AI
- 116. #denatechcon 7 1 Video Frame Synthesis using Deep Voxel Flow. Ziwei Liu, Raymond A. Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala. In ICCV 2017.
- 117. #denatechcon N I 7 B7 =: B = P 77 = 7: :=D • /0 (+ /0 , Video Frame Synthesis using Deep Voxel Flow. Ziwei Liu, Raymond A. Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala. In ICCV 2017. BB F=CBC 67 ?. / 3: 1 B Video Frame Synthesis using Deep Voxel Flow
- 118. #denatechcon D F 6 + 23C • 1 76 , P SV P J IOM S R • ( ,24 c SV P J ,24 cP / ++ C • 1 76 , P J SV P • 4 8 4 0 L a Super SloMo(Adobe) Super SloMo Deep Voxel Flow Video Frame Synthesis using Deep Voxel Flow. Ziwei Liu, Raymond A. Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala. In ICCV 2017. Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation. Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz. In CVPR 2018. F
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- 136. #denatechcon Conv-BN-ReLU Conv-BN-ReLU Conv-BN-ReLU Conv-BN-ReLU Local Discriminator “Real” or “Fake” Local Patch (16×16pix)
- 137. #denatechcon Conv-BN-ReLU Conv-BN-ReLU Generated Image Sequense Conv-BN-ReLU Conv-BN-ReLU Conv-BN-ReLU Conv-BN-ReLU Conv-BN-ReLU Conv-BN-ReLU Conv-BN-ReLU FC Local Discriminator Temporal Discriminator “Real” or “Fake” Local Patch (16×16pix) Image Sequense “Real” or “Fake”
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- 141. #denatechcon ⁃ step size = 4 7FPS -> 30FPS 001.png, 005.png, 009.png, 013.png, 017.png ⁃ step size = 1 30FPS -> 120FPS 001.png, 002.png, 003.png, 004.png, 005.png
- 142. #denatechcon ⁃ step size = 4 7FPS -> 30FPS 001.png, 005.png, 009.png, 013.png, 017.png ⁃ step size = 1 30FPS -> 120FPS 001.png, 002.png, 003.png, 004.png, 005.png
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- 144. #denatechcon Frame Frame Deep Voxel FlowInput // . / Experimental Results: “Anime Frame Generation with Structure-consistent Prediction GANs”
- 145. #denatechcon step size = 1 step size = 4 step size = 7 step size = 10 Input SPGAN (Ours) Deep Voxel Flow 4 // . / Experimental Results: “Anime Frame Generation with Structure-consistent Prediction GANs”
- 146. #denatechcon Deep Voxel Flow Ours 1.average PSNR/SSIM on test dataset step size=4 PSNR SSIM Deep Voxel Flow 23.32 0.9294 SPGAN(Ours) 24.27 0.9407
- 147. #denatechcon AGENDA
- 148. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
- 149. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
- 150. #denatechcon ProgressiveGAN (Karras et al., ICLR 2018) BigGAN (Brock et al., ICLR 2019)
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- 159. #denatechcon TZ ... ./ 0 KD SL KA N O N KD SL K N W (3 Frame) AI (x16 ) Input Frames Generated Frames 78 : : 102*0DeNA AI :
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