Advanced search
Publication Cover
Connection Science Volume 36, 2024 - Issue 1
Submit an article Journal homepage
363
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Contour wavelet diffusion – a fast and high-quality facial expression generation model

Chenwei Xua School of Design and Art, Communication University of Zhejiang, Hangzhou, ChinaView further author information
&
Yuntao Zoub School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaCorrespondence[email protected]
View further author information
Article: 2316023 | Received 11 Sep 2023, Accepted 02 Feb 2024, Published online: 14 Feb 2024

References

  • Abdal, R., Qin, Y., & Wonka, P. (2019). Image2stylegan: How to embed images into the stylegan latent space? 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00453
  • Bamberger, R. H., & Smith, M. J. (1992). A filter bank for the directional decomposition of images: Theory and design. IEEE Transactions on Signal Processing, 40(4), 882–893. https://doi.org/10.1109/78.127960
  • Batista, J. C., Albiero, V., Bellon, O. R., & Silva, L. (2017). Aumpnet: Simultaneous action units detection and intensity estimation on multipose facial images using a single convolutional neural network. 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2017.
  • Brophy, E., Wang, Z., She, Q., & Ward, T. (2023). Generative adversarial networks in time series: A systematic literature review. ACM Computing Surveys, 55(10), Article 199. https://doi.org/10.1145/3559540
  • Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., & Lundberg, S. (2023). Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:.12712.
  • Carvalho, T. G., Thiberge, S., Sakamoto, H., & Ménard, R. (2004). Conditional mutagenesis using site-specific recombination in Plasmodium berghei. Proceedings of the National Academy of Sciences, 101(41), 14931–14936. https://doi.org/10.1073/pnas.0404416101
  • Croitoru, F.-A., Hondru, V., Ionescu, R. T., & Shah, M. (2023). Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9), 10850–10869. https://doi.org/10.1109/tpami.2023.3261988
  • Da Cunha, A. L., Zhou, J., & Do, M. N. (2006). The nonsubsampled contourlet transform: Theory, design, and applications. IEEE Transactions on Image Processing, 15(10), 3089–3101. https://doi.org/10.1109/TIP.2006.877507
  • Dhariwal, P., & Nichol, A. (2021). Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34, 8780–8794.
  • Do, M. N., & Vetterli, M. (2003). The finite ridgelet transform for image representation. IEEE Transactions on Image Processing, 12(1), 16–28. https://doi.org/10.1109/TIP.2002.806252
  • Ekman, P. (2004). Emotions revealed. BMJ, 328(Suppl S5), 0405184. https://doi.org/10.1136/sbmj.0405184
  • Ekman, P., & Friesen, W. V. (1978). Facial action coding system [dataset]. In PsycTESTS Dataset. American Psychological Association (APA). https://doi.org/10.1037/t27734-000
  • Ekman, P., Friesen, W. V., & Tomkins, S. S. (1971). Facial affect scoring technique: A first validity study.
  • Eslami, R., & Radha, H. (2004). Wavelet-based contourlet transform and its application to image coding. 2004 International Conference on Image Processing, 2004. ICIP'04.
  • Esser, P., Rombach, R., Blattmann, A., & Ommer, B. (2021). Imagebart: Bidirectional context with multinomial diffusion for autoregressive image synthesis. Advances in Neural Information Processing Systems, 34, 3518–3532.
  • Freeman, W. T., & Adelson, E. H. (1991). The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(9), 891–906. https://doi.org/10.1109/34.93808
  • Gao, R., Song, Y., Poole, B., Wu, Y. N., & Kingma, D. P. (2020). Learning energy-based models by diffusion recovery likelihood. arXiv preprint arXiv:.08125.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Stat, 10, 1050.
  • Gudi, A., Tasli, H. E., Den Uyl, T. M., & Maroulis, A. (2015). Deep learning based facs action unit occurrence and intensity estimation. 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
  • Gupta, G., Khan, S., Guleria, V., Almjally, A., Alabduallah, B. I., Siddiqui, T., Albahlal, B. M., Alajlan, S. A., & Al-Subaie, M. (2023). DDPM: A dengue disease prediction and diagnosis model using sentiment analysis and machine learning algorithms. Diagnostics, 13(6), 1093. https://doi.org/10.3390/diagnostics13061093
  • Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840–6851.
  • Huang, J., Cui, K., Guan, D., Xiao, A., Zhan, F., Lu, S., Liao, S., & Xing, E. (2022). Masked generative adversarial networks are data-efficient generation learners. Advances in Neural Information Processing Systems, 35, 2154–2167.
  • Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:.10196.
  • Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020). Analyzing and improving the image quality of stylegan. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.
  • Kaur, G., Agarwal, R., & Patidar, V. (2020). Semi-blind robust watermarking with dual complex tree wavelet based hybrid transform and SVD. 2020 IEEE 17th India Council International Conference (INDICON).
  • Kingma, D. P., & Dhariwal, P. (2018, December). Glow: Generative flow with invertible 1 × 1 convolutions. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (pp. 10236–10245).
  • Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
  • Kong, Z., & Ping, W. (2021). On fast sampling of diffusion probabilistic models. arXiv preprint arXiv:.00132.
  • Lu, Y., Chen, D., Olaniyi, E., & Huang, Y. (2022). Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review. Computers and Electronics in Agriculture, 200, 107208. https://doi.org/10.1016/j.compag.2022.107208
  • Luhman, E., & Luhman, T. (2021). Knowledge distillation in iterative generative models for improved sampling speed. arXiv preprint arXiv:.02388.
  • Ma, H., Zhang, L., Zhu, X., & Feng, J. (2022). Accelerating score-based generative models with preconditioned diffusion sampling. European Conference on Computer Vision.
  • Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., & Chen, M. (2021). Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:.10741.
  • Obukhov, A., & Krasnyanskiy, M. (2020). Quality assessment method for GAN based on modified metrics inception score and Fréchet inception distance. Advances in Intelligent Systems and Computing, 102–114. https://doi.org/10.1007/978-3-030-63322-6_8
  • Oord, A. V. D., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., & Kavukcuoglu, K. (2016, December). Conditional image generation with PixelCNN decoders. In Proceedings of the 30th International Conference on Neural Information Processing Systems (pp. 4797–4805).
  • Pandzic, I. S. (2002). MPEG-4 facial animation framework for the web and mobile applications. In MPEG-4 Facial Animation (pp. 65–79). Portico. https://doi.org/10.1002/0470854626.ch4
  • Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:.06125, 1(2), 3.
  • Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021). Zero-shot text-to-image generation. International Conference on Machine Learning.
  • Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.
  • Salimans, T., & Ho, J. (2022). Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:.00512.
  • Shensa, M. J. (1992). The discrete wavelet transform: Wedding the a trous and Mallat algorithms. IEEE Transactions on Signal Processing, 40(10), 2464–2482. https://doi.org/10.1109/78.157290
  • Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep unsupervised learning using nonequilibrium thermodynamics. International Conference on Machine Learning.
  • Song, J., Meng, C., & Ermon, S. (2020). Denoising diffusion implicit models. arXiv preprint arXiv:.02502.
  • Song, Y., Durkan, C., Murray, I., & Ermon, S. (2021). Maximum likelihood training of score-based diffusion models. Advances in Neural Information Processing Systems, 34, 1415–1428.
  • Song, Y., & Ermon, S. (2020). Improved techniques for training score-based generative models. Advances in Neural Information Processing Systems, 33, 12438–12448.
  • Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2020). Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:.13456.
  • Taubman, D. S., Marcellin, M. W., & Rabbani, M. (2002). JPEG2000: Image compression fundamentals, standards and practice. Journal of Electronic Imaging, 11(2), 286–287. https://doi.org/10.1117/1.1469618
  • Tran, D. L., Walecki, R., Rudovic, O., Eleftheriadis, S., Schuller, B., & Pantic, M. (2017). Deepcoder: Semi-parametric variational autoencoders for automatic facial action coding. 2017 IEEE International Conference on Computer Vision (ICCV).
  • Vahdat, A., & Kautz, J. J. A. i. n. i. p. s. (2020). NVAE: A deep hierarchical variational autoencoder. 33, 19667–19679.
  • Vahdat, A., Kreis, K., & Kautz, J. (2021). Score-based generative modeling in latent space. Advances in Neural Information Processing Systems, 34, 11287–11302.
  • Van Erven, T., & Harremos, P. (2014). Rényi divergence and Kullback-Leibler divergence. IEEE Transactions on Information Theory, 60(7), 3797–3820. https://doi.org/10.1109/TIT.2014.2320500
  • Vincent, P. (2011). A connection between score matching and denoising autoencoders. Neural Computation, 23(7), 1661–1674. https://doi.org/10.1162/NECO_a_00142
  • Walecki, R., Rudovic, O., Pavlovic, V., & Pantic, M. (2016). Copula ordinal regression for joint estimation of facial action unit intensity. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Walecki, R., Rudovic, O., Pavlovic, V., & Pantic, M. (2017). Variable-state latent conditional random field models for facial expression analysis. Image and Vision Computing, 58, 25–37. https://doi.org/10.1016/j.imavis.2016.04.009
  • Wang, Z., Zheng, H., He, P., Chen, W., & Zhou, M. (2022). Diffusion-gan: Training gans with diffusion. arXiv preprint arXiv:.02262.
  • Xiao, Z., Kreis, K., Kautz, J., & Vahdat, A. (2020). Vaebm: A symbiosis between variational autoencoders and energy-based models. arXiv preprint arXiv:.00654.
  • Yang, M., Wang, Z., Chi, Z., & Feng, W. (2022). WaveGAN: Frequency-aware GAN for high-fidelity few-shot image generation. European Conference on Computer Vision.
  • Zhang, S., Li, L., & Zhao, Z. (2012). Facial expression recognition based on Gabor wavelets and sparse representation. 2012 IEEE 11th International Conference on Signal Processing.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.