Document Type
Conference Proceeding
Publication Date
10-26-2025
Publisher
Association for Computing Machinery
Abstract
The gaming and entertainment industry is rapidly evolving, driven by immersive experiences and the integration of generative AI (GAI) technologies. Training such models effectively requires large-scale datasets that capture the diversity and context of gaming environments. However, existing datasets are often limited to specific domains or rely on artificial degradations, which do not accurately capture the unique characteristics of gaming content. Moreover, benchmarks for controllable video generation remain absent. To address these limitations, we introduce M3VIR, a large-scale, multi-modal, multi-view dataset specifically designed to overcome the shortcomings of current resources. Unlike existing datasets, M3VIR provides diverse, high-fidelity gaming content rendered with Unreal Engine 5, offering authentic ground-truth LR-HR paired and multi-view frames across 80 scenes in 8 categories. It includes M3VIR_MR for super-resolution (SR), novel view synthesis (NVS), and combined NVS+SR tasks, and M3VIR_MS, the first multi-style, object-level ground-truth set enabling research on controlled video generation. Additionally, we benchmark several state-of-the-art SR and NVS methods to establish performance baselines. While no existing approaches directly handle controlled video generation, M3VIR provides a benchmark for advancing this area. By releasing the dataset, we aim to facilitate research in AI-powered restoration, compression, and controllable content generation for next-generation cloud gaming and entertainment.
Recommended Citation
Li, Y., Zhou, L., Ling, N., Chen, Z., Wang, W., & Jiang, W. (2025). M3VIR: A Large-Scale Multi-Modality Multi-View Synthesized Benchmark Dataset for Image Restoration and Content Creation. Proceedings of the 3rd International Workshop on Rich Media With Generative AI, RichMediaGAI ’25, 20–29. https://doi.org/10.1145/3746262.3761976
Comments
Open access to this article is funded by Santa Clara University Library.
This work is licensed under Creative Commons Attribution International 4.0.