Revisiting the Uncanny Valley in the AI Era: A Review of Visual Artifacts and Aesthetic Perceptions in AI-Generated Characters
DOI:
https://doi.org/10.65150/EP-jsshrs/V2E10/2026-18Keywords:
Generative AI, Uncanny Valley, Visual Artifacts, Digital Character Design, Post-Digital Aesthetics, Visual Storytelling, Human-AI Collaboration.Abstract
The rapid evolution of generative artificial intelligence (AI), particularly diffusion-based image synthesis models, has fundamentally transformed digital character design and visual storytelling. However, the frequent occurrence of "visual artifacts"—such as anatomical distortions, unnatural skin smoothness, rigid facial expressions, and latent space blending—presents significant aesthetic and psychological challenges. This paper provides a critical, multidisciplinary review that re-examines the classic "Uncanny Valley" hypothesis within the contemporary context of AI-generated media. Moving beyond the conventional engineering perspective that views these artifacts merely as technical deficiencies to be eliminated, this study investigates their potential reinterpretation through the lenses of post-digital media art, glitch aesthetics, and visual semiotics. We analyze how audience perception, cognitive friction, and emotional engagement are affected by these synthetic cues. Furthermore, we explore how human designers utilize advanced node-based control frameworks (e.g., ComfyUI, ControlNet) to negotiate, fix, or intentionally embrace these algorithmic anomalies. Finally, we establish a conceptual framework for human-AI co-creation and highlight future research trajectories for character narrative design in generative media.
References
1. Borji, A. (2023). A categorical archive of ChatGPT failures. arXiv preprint arXiv:2302.03494.
2. Burleigh, T. J., Schoenherr, J. R., & Lacroix, G. L. (2013). Does the uncanny valley exist? An empirical test of the relationship between eeriness and the human likeness of digitally created faces. Computers in Human Behavior, 29(3), 759-771.
3. Cascone, K. (2000). The aesthetics of failure: "Post-digital" tendencies in contemporary computer music. Computer Music Journal, 24(4), 12-18.
4. Catmull, E. (1978). A hidden-surface algorithm with anti-aliasing. ACM SIGGRAPH Computer Graphics, 12(3), 6-11.
5. Ferrey, A. E., Burleigh, T. J., & Fenske, M. J. (2015). Stimulus-category competition, inhibition, and affective devaluation: A novel account of the uncanny valley. Frontiers in Psychology, 6, 249.
6. Freud, S. (1919). Das Unheimliche [The Uncanny]. Imago, 5(5), 297-324.
7. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems (NeurIPS), 27, 2672-2680.
8. Green, M. C., & Brock, T. C. (2000). The role of transportation in the persuasiveness of public narratives. Journal of Personality and Social Psychology, 79(5), 701-721.
9. Haraway, D. (1991). A cyborg manifesto: Science, technology, and socialist-feminism in the late twentieth century. Simians, Cyborgs, and Women: The Reinvention of Nature, 149-181. Routledge.
10. Hayles, N. K. (1999). How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. University of Chicago Press.
11. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., ... & Chen, W. (2021). Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685.
12. Jentsch, E. (1906). On the psychology of the uncanny. Psychiatrisch-Neurologische Wochenschrift, 8(22), 195-198.
13. Kätsyri, J., Förger, K., Mäkäräinen, M., & Takala, T. (2015). A review of empirical evidence on different uncanny valley hypotheses: Support for perceptual mismatch as one road to the valley of eeriness. Frontiers in Psychology, 6, 390.
14. MacDorman, K. F., & Ishiguro, H. (2006). The uncanny advantage of using androids in cognitive and social science research. Interaction Studies, 7(3), 297–337.
15. McGloin, R., Farrar, K. M., & Krcmar, M. (2011). The impact of controller naturalness on spatial presence, gamer enjoyment, and perceived realism in a tennis simulation video game. Presence: Teleoperators and Virtual Environments, 20(4), 309–324.
16. Mori, M. (1970). Bukimi no tani [The uncanny valley]. Energy, 7(4), 33-35.
17. Mori, M., MacDorman, K. F., & Kageki, N. (2012). The uncanny valley [from the field]. IEEE Robotics & Automation Magazine, 19(2), 98-100.
18. Pepperell, R. (2003). The Posthuman Condition: Consciousness Beyond the Brain. Intellect Books.
19. Poole, B., Jain, A., Barron, J. T., & Mildenhall, B. (2022). Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988.
20. Prince, S. (2011). Digital Visual Effects in Cinema: The Seduction of Reality. Rutgers University Press.
21. MacDorman, K. F., & Chattopadhyay, D. (2016). Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not. Cognition, 146, 190–205.
22. 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 (CVPR), 10684-10695.
23. Seyama, J., & Nagai, R. S. (2007). The uncanny valley: Effect of realism on the impression of artificial faces. Presence: Teleoperators and Virtual Environments, 16(4), 337-351.
24. Zhang, L., Rao, A., & Agrawala, M. (2023). Adding conditional control to text-to-image diffusion models. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 3836-3847.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Muhan Li (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
