Title: Digital media art design mechanism based on reinforcement learning in smart city

Authors: Xin He

Addresses: Xinxiang Radio and Television University, Ürümqi, Xinxiang, China

Abstract: Digital media art has emerged as a pivotal domain that intersects technology, culture and urban life, transforming public spaces and offering novel forms of interaction and expression. In this paper, we propose a novel framework that leverages Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) for 3D face reconstruction in digital media art design. We train and evaluate our model with rigorous experiments based on public data set, comparing its performance against several state-of-the-art methods. Our proposed model demonstrates superior performance in two metrics. Additionally, we conduct convergence analysis and robustness to input noise experiments to further validate our approach. The results highlight the effectiveness of our method in producing high-quality, realistic and robust 3D face reconstructions, underscoring its potential for enhancing digital media art installations in smart cities.

Keywords: digital media; smart city; reinforcement learning.

DOI: 10.1504/IJCAT.2025.150329

International Journal of Computer Applications in Technology, 2025 Vol.77 No.3/4, pp.238 - 246

Received: 04 Sep 2024
Accepted: 24 May 2025
Published online: 09 Dec 2025 *

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