Yeong-Gi Hong
ABSTRACT
Play and games are an inseparable part of our lives, and we have been playing various games from our childhood to adulthood. One of the widely recognized games is "Spot-the-Difference", which has been played and employed in a variety of domains, such as education, training, and entertainment. However, designing a custom "Spot-the-Difference" game for a certain domain requires consideration in terms of the level of game difficulty, game interest, and human intervention, therefore, is non-trivial. In this paper, we propose a novel framework based on Human-AI collaboration to automatically generate images for the "Spot-the-Difference" game. We employ Maskformer and Inpainting Stable Diffusion to automatically identify and re-draw a set of regions in the image. Finally, we conducted a user study with 19 participants to evaluate our framework. From the experimental result, we found that AI-generated game images were enjoyable, but there is still room for improvement in the quality of the overall game playing.
Supplemental Material
- Brandon Burr, Ladonna Atkins, Anita Glee Bertram, Kaye Sears, and Andreae N McGinnis. 2019. “If you stop playing you get old”: investigating reflections of play in older adults. Educational Gerontology 45, 5 (2019), 353–364.Google Scholar
Cross Ref
- Jeongho Cha, Su Yin Kan, and Poh Wai Chia. 2018. Spot the differences Game: An Interactive method that engage students in organic chemistry learning. Journal of Korean chemical society (2018), 159–162.Google Scholar
- Bowen Cheng, Omkar Parkhi, and Alexander Kirillov. 2022. Pointly-supervised instance segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2617–2626.Google ScholarCross Ref
- Bowen Cheng, Alex Schwing, and Alexander Kirillov. 2021. Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems 34 (2021), 17864–17875.Google Scholar
- Diane E Frey. 1994. The use of play therapy with adults. Handbook of play therapy 2 (1994), 189–205.Google Scholar
- Peter Gray. 2015. The decline of play | Peter Gray | TEDxNavesink. Retrieved Accessed 19-Jan-2023 from https://www.youtube.com/watch?v=Bg-GEzM7iTkGoogle Scholar
- Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. 2022. Cascaded Diffusion Models for High Fidelity Image Generation.J. Mach. Learn. Res. 23 (2022), 47–1.Google Scholar
- Yaosi Hu, Chong Luo, and Zhenzhong Chen. 2022. Make it move: controllable image-to-video generation with text descriptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18219–18228.Google ScholarCross Ref
- Ngan Kuen Lai, Tan Fong Ang, Lip Yee Por, and Chee Sun Liew. 2018. The impact of play on child development-a literature review. European Early Childhood Education Research Journal 26, 5 (2018), 625–643.Google ScholarCross Ref
- Yanfen Li, Hanxiang Wang, L Minh Dang, Md Jalil Piran, and Hyeonjoon Moon. 2022. A robust instance segmentation framework for underground sewer defect detection. Measurement 190 (2022), 110727.Google ScholarCross Ref
- Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In European conference on computer vision. Springer, 740–755.Google ScholarCross Ref
- Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2021. High-Resolution Image Synthesis with Latent Diffusion Models. arxiv:2112.10752 [cs.CV]Google Scholar
- Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. 2022. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings. 1–10.Google ScholarDigital Library
- Robyn Schell, Simone Hausknecht, Fan Zhang, and David Kaufman. 2016. Social benefits of playing Wii Bowling for older adults. Games and Culture 11, 1-2 (2016), 81–103.Google ScholarCross Ref
- Liu Yang, Wen Li, Jian Zou, Junnan An, Bin Zeng, Yitao Zheng, Jiming Yang, and Jia Ren. 2022. The application of the spot the difference teaching method in clinical skills training for residents. BMC Medical Education 22, 1 (2022), 1–10.Google ScholarCross Ref
- Tianwen Zhang and Xiaoling Zhang. 2022. A mask attention interaction and scale enhancement network for SAR ship instance segmentation. IEEE Geoscience and Remote Sensing Letters 19 (2022), 1–5.Google Scholar
Index Terms
- Spot The Difference: AI, Please Make This for Me!
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