Title: Efficient lightweight AI-driven animation generation for IoT systems via dynamic encoding and contrastive learning

Authors: Peng Guo

Addresses: Art School, Wuyi University, Wuyishanshi, 354300, Fujian, China

Abstract: We introduce efficient lightweight AI-driven animation generation, named ELA-AG, a novel framework tailored for IoT systems demanding real-time performance under stringent resource constraints. ELA-AG innovates by combining three core components: 1) dynamic encoding to compress temporal-spatial redundancies across adjacent frames; 2) contrastive animation learning to enforce latent alignment across time and eliminate flicker; 3) a resource-aware optimisation objective that integrates model efficiency metrics (FLOPs, memory footprint, energy, latency) directly into training. We evaluate ELA-AG across three datasets - AnimeRun, CartoonMotion-50K and our IoT-AnimeCam - with comparisons against CartoonGAN, AnimeGAN2, DualAST and other baselines. Quantitative results demonstrate that ELA-AG achieves superior perceptual quality (PSNR, SSIM, FID) and temporal coherence (LPIPS, TWE), while achieving notable improvements in efficiency - higher FPS, halved energy consumption, and reduced model size. Comprehensive ablations confirm that ELA-AG achieves a 47% reduction in energy consumption, 1.5× higher FPS, and a 19.2% lower FID compared with DualAST, while preserving superior temporal coherence. These results set a new benchmark for Pareto-efficient animation synthesis on resource-constrained IoT platforms, ensuring high-quality, temporally consistent animations without sacrificing speed or energy.

Keywords: efficient animation generation; lightweight AI; dynamic encoding; contrastive animation learning; resource-aware optimisation; temporal coherence; energy-aware AI.

DOI: 10.1504/IJBIDM.2026.155255

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.4/5/6, pp.317 - 330

Received: 22 Sep 2025
Accepted: 13 Jan 2026
Published online: 29 Jul 2026 *

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