INTERNATIONAL JOURNAL OF MEDIA AND COMMUNICATIONS IN CENTRAL ASIA

No. 13 (2026): International journal of media and communications in Central Asia
Статьи

INTEGRATION OF MOBILEGRAPHY AND CAMERA MOVEMENTS IN NEUROPHOTOSHOOTS: ENHANCING VISUAL REALISM THROUGH PROMPT ENGINEERING

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  • Gulshan Kayumova ,
  • Dilora Fayzullayeva,
  • Boburmirzo Yigitaliyev
Gulshan Kayumova
University of Journalism and Mass Communications of Uzbekistan
Dilora Fayzullayeva
University of Journalism and Mass Communications of Uzbekistan
Boburmirzo Yigitaliyev
University of Journalism and Mass Communications of Uzbekistan
DOI: https://doi.org/10.62499/ijmcc.vi13.296

Published 2026-05-22

Keywords

  • neuro-photoshoot,
  • mobilegraphy,
  • prompt engineering,
  • camera angles,
  • camera movements,
  • diffusion models,
  • visual realism
    • ...More
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How to Cite

Kayumova , G., Fayzullayeva, D., & Yigitaliyev , B. (2026). INTEGRATION OF MOBILEGRAPHY AND CAMERA MOVEMENTS IN NEUROPHOTOSHOOTS: ENHANCING VISUAL REALISM THROUGH PROMPT ENGINEERING. INTERNATIONAL SCIENTIFIC JOURNAL OF MEDIA AND COMMUNICATIONS IN CENTRAL ASIA, (13). https://doi.org/10.62499/ijmcc.vi13.296

Abstract

This article analyzes the integration of mobilegraphy, camera angles, and camera movements into artificial intelligence (AI) systems through prompt engineering in the process of neuro-photoshoots. Although modern text-to-image diffusion models offer significant capabilities for generating visual content, the quality of their output largely depends on the accuracy and structural clarity of the input prompt.  The study formalizes camera angles (eye-level, low angle, high angle, etc.) and camera movements (pan, tilt, dolly, handheld) within a structured prompt format in combination with mobilegraphy principles. The results demonstrate that such an approach significantly enhances the realism, depth, and cinematic quality of generated images.

PDF (O'zbekcha)

References

  1. Aitken, A. P., Ledig, C., Theis, L., Caballero, J., Wang, Z., & Shi, W. (2017). Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize. arXiv preprint arXiv:1707.02937 https://doi.org/10.48550/.
  2. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021). On the opportunities and risks of foundation models. arXiv. https://arxiv.org/abs/2108.07258
  3. Bordwell, D., & Thompson, K. (2019). Film art: An introduction (12th ed.). McGraw-Hill Education.
  4. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems, 27. pp. 2672–2680. Retrieved May 07, 2026 from https://papers.nips.cc/paper/5423-generative-adversarial-nets
  5. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, 33. pp. 6840–6851). Retrieved May 07, 2026 from https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html
  6. Herrera, L., Schaefer, K. L., Benjamin, L. S. S., & Henderson, J. A. (2023). Flash On: Capturing Minoritized Engineering Students’ Persistence through Photovoice Research. Sustainability, 15(6), 5311. https://doi.org/10.3390/su15065311
  7. Haugsbaken, H. and Hagelia, M., (2024) A New AI Literacy For The Algorithmic Age: Prompt Engineering Or Eductional Promptization?, 4th International Conference on Applied Artificial Intelligence (ICAPAI), Halden, Norway, 2024, pp. 1-8, doi: 10.1109/ICAPAI61893.2024.10541229.
  8. Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., & Zhu, J. (2022). DPM-Solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps. In Advances in Neural Information Processing Systems (Vol. 35, pp. 5775–5787). Retrieved May 07, 2026 from https://arxiv.org/abs/2206.00927
  9. Manovich, L. (2020). Cultural analytics. MIT Press. Retrieved May 07, 2026 from https://mitpress.mit.edu/9780262037105/cultural-analytics/
  10. Nichol, A. Q., & Dhariwal, P. (2021). Improved denoising diffusion probabilistic models. In Proceedings of the 38th International Conference on Machine Learning (pp. 8162–8171). Retrieved May 07, 2026 from https://proceedings.mlr.press/v139/nichol21a.html
  11. Odena, A., Dumoulin, V., & Olah, C. (2016). Deconvolution and checkerboard artifacts. Distill. https://doi.org/10.23915/distill.00003
  12. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (pp. 8748–8763). Retrieved May 07, 2026 from https://proceedings.mlr.press/v139/radford21a.html
  13. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with CLIP latents. arXiv. Retrieved May 07, 2026 from https://arxiv.org/abs/2204.06125
  14. Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al. (2022). Photorealistic text-to-image diffusion models with deep language understanding. In Advances in Neural Information Processing Systems (Vol. 35). Retrieved May 07, 2026 from https://arxiv.org/abs/2205.11487
  15. Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., & Ganguli, S. (2015). Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the 32nd International Conference on Machine Learning (pp. 2256–2265). Retrieved May 07, 2026 from https://proceedings.mlr.press/v37/sohl-dickstein15.html
  16. Shi, C. and Yang, S., (2023) LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models, IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 2023, pp. 2920-2929, doi: 10.1109/ICCV51070.2023.00274.
  17. Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018). The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 586–595). Retrieved May 07, 2026 from https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_The_Unreasonable_Effectiveness_CVPR_2018_paper.html
  18. Zhan, ZZ., Xiong, YT., Wang, CY. et al. (2025). Utilizing GPT-4 to interpret oral mucosal disease photographs for structured report generation. Sci Rep 15, 5187 https://doi.org/10.1038/s41598-025-89328-y