Abstract
As an important part of animation production, the existing method for drawing and rendering the CG animated characters according to motion information mostly relays on expensive manual processing. By adopting the conditional adversarial learning, an automatic animation rendering system for geometry structure attribute is proposed, using a deep convolution generative adversarial network called “pix2pixHD”. A training database containing a variety of motion stick figure is established for different virtual characters to achieve an end-to-end training system to verify this idea. The trained generator is the desired CG animation creator which shows great performance on visual quality and time efficiency proved by the experimental results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291–7299 (2017)
Chan, C., Ginosar, S., Zhou, T., Efros, A.A.: Everybody dance now. arXiv preprint arXiv:1808.07371 (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Hamada, K., Tachibana, K., Li, T., Honda, H., Uchida, Y.: Full-body high-resolution anime generation with progressive structure-conditional generative adversarial networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11131, pp. 67–74. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11015-4_8
Han, X., Gao, C., Yu, Y.: Deepsketch2face: a deep learning based sketching system for 3D face and caricature modeling. ACM Trans. Graph. (TOG) 36(4), 126 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Jin, Y., Zhang, J., Li, M., Tian, Y., Zhu, H.: Towards the high-quality anime characters generation with generative adversarial networks. In: Proceedings of the Machine Learning for Creativity and Design Workshop at NIPS (2017)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Korshunov, P., Marcel, S.: Deepfakes: a new threat to face recognition? Assessment and detection. arXiv preprint arXiv:1812.08685 (2018)
Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300 (2015)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Qiao, F., Yao, N., Jiao, Z., Li, Z., Chen, H., Wang, H.: Geometry-contrastive generative adversarial network for facial expression synthesis. CoRR abs/1802.01822 (2018)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Valentin, J., Keskin, C., Pidlypenskyi, P., Makadia, A., Sud, A., Bouaziz, S.: TensorFlow graphics: computer graphics meets deep learning (2019)
Vondrick, C., Pirsiavash, H., Torralba, A.: Generating videos with scene dynamics. In: Advances in Neural Information Processing Systems, pp. 613–621 (2016)
Wang, T.C., et al.: Video-to-video synthesis. arXiv preprint arXiv:1808.06601 (2018)
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8798–8807 (2018)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724–4732 (2016)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, J., Cui, J., Shi, G., Liu, D. (2019). CG Animation Creator: Auto-rendering of Motion Stick Figure Based on Conditional Adversarial Learning. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-31726-3_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31725-6
Online ISBN: 978-3-030-31726-3
eBook Packages: Computer ScienceComputer Science (R0)