Fetching the paper…
Reading the bibliography…
Story visualization aims to generate a series of realistic and coherent images based on a storyline.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML (2015)
2015
Earlier work this paper cites.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., Metaxas, D.N.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: ICCV (2017)
2017
Earlier work this paper cites.
Gupta, T., Schwenk, D., Farhadi, A., Hoiem, D., Kembhavi, A.: Imagine this! scripts to compositions to videos. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 598–613 (2018)
2018
Earlier work this paper cites.
Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., He, X.: Attngan: Fine-grained text to image generation with attentional generative adversarial networks. In: CVPR (2018)
2018
Earlier work this paper cites.
Li, Y., Gan, Z., Shen, Y., Liu, J., Cheng, Y., Wu, Y., Carin, L., Carlson, D., Gao, J.: Storygan: A sequential conditional gan for story visualization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6329–6338 (2019)
2019
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
2019
Earlier work this paper cites.
Zhu, M., Pan, P., Chen, W., Yang, Y.: Dm-gan: Dynamic memory generative adversarial networks for text-to-image synthesis. In: CVPR (2019)
2019
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. In: NeurIPS (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. NeurIPS (2020)
2020
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR 21
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Song, Y.Z., Rui Tam, Z., Chen, H.J., Lu, H.H., Shuai, H.H.: Character-preserving coherent story visualization. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII 16. pp. 18–33. Springer (2020)
2020
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. NeurIPS (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: ICML (2021)
Mangrulkar, S., Gugger, S., Debut, L., Belkada, Y., Paul, S., Bossan, B.: Peft: State-of-the-art parameter-efficient fine-tuning methods. https://github.com/huggingface/peft (2022)
2022
Later among the works it cites.
Nichol, A.Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., Chen, M.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In: ICML (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation. In: ICML (2021)
2021
Cited alongside, same era.
2022
Cited alongside, same era.
Gafni, O., Polyak, A., Ashual, O., Sheynin, S., Parikh, D., Taigman, Y.: Make-a-scene: Scene-based text-to-image generation with human priors. In: ECCV (2022)
2022
Cited alongside, same era.
Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., Guo, B.: Vector quantized diffusion model for text-to-image synthesis. In: CVPR (2022)
2022
Cited alongside, same era.
Ho, J., Saharia, C., Chan, W., Fleet, D.J., Norouzi, M., Salimans, T.: Cascaded diffusion models for high fidelity image generation. JMLR 23
2022
Cited alongside, same era.
Li, B.: Word-level fine-grained story visualization. In: European Conference on Computer Vision. pp. 347–362. Springer (2022)
2022
Cited alongside, same era.
Li, B., Torr, P.H., Lukasiewicz, T.: Clustering generative adversarial networks for story visualization. In: Proceedings of the 30th ACM International Conference on Multimedia. pp. 769–778 (2022)
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Tao, M., Tang, H., Wu, F., Jing, X.Y., Bao, B.K., Changsheng, X.: Df-gan: A simple and effective baseline for text-to-image synthesis. In: CVPR (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Rahman, T., Lee, H.Y., Ren, J., Tulyakov, S., Mahajan, S., Sigal, L.: Make-a-story: Visual memory conditioned consistent story generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2493–2502 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.