Image Processing and Virtual Reality

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Image Processing and Virtual Reality Group

Professors: Jong-Il Park, Euee S. Jang, Taesoo Kwon, Kwangwook Kim, Yoonsang Lee, Tae Hyun Kim, Donghyeon Cho

Image processing is handling images by using mathematical operations. Image-processing techniques involve treating the image as the generation, processing, analysis, and recognition of two or three dimensional signals. Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting. Computer vision is often considered high-level image processing to decipher the physical contents of images.
Virtual reality (VR) is a computer technology that uses software-generated realistic images, sounds and other sensations to replicate real/imaginary environments. A person using VR equipment is usually able to "look around" the artificial world, move and interact with the features or items that are depicted inside.

Selected Publications

  • Kang, K., T. Gu, and Taesoo Kwon. “Learning climbing controllers for physics-based characters.” Computer Graphics Forum 44, no. 1 (2025): e15284.
  • Park, G., J. Hwang, and Taesoo Kwon. “Sample-efficient reference-free control strategy for multi-legged locomotion.” Computers & Graphics 126 (2025): 104141.
  • Kang, M., M. Jung, N. An, and Kwanguk Kim. “Physical synchrony through a beamed robot enhances social closeness and inter-brain synchrony.” In CHI Conference on Human Factors in Computing Systems Extended Abstracts (Article 514, pp. 1-7). ACM, 2026.
  • Kim, J., M. Jung, D. Kang, T. J. Rhee, D. Kim, and Kwanguk Kim. “Affective and cognitive feedback from a robot for human-attributed failure handling.” In CHI Conference on Human Factors in Computing Systems (Article 819, pp. 1-16). ACM, 2026.
  • Oh, H., N. Hong, Kwanguk Kim, D.-H. Oh, J.-J. Kim, and E. Kim. “Stepping into Virtual School: A pilot study of VR-based social skills training (SST) integrating emotion- and knowledge-based approaches.” Virtual Reality 30, no. 1 (2026): 40.
  • Woo, Y., S. Hwang, S. Oh, D. Kang, M. Jung, and Kwanguk Kim. “Human activity recognition dataset for pedestrians with mobility disabilities.” Scientific Data 13 (2026): 211.
  • You, J., J. Heo, J.-S. Sunwoo, K.-Y. Jung, and Kwanguk Kim. “Ten-scenarios of tablet-based virtual environment game for instrumental activities of daily living in iRBD patients.” IEEE Transactions on Games 17, no. 4 (2025): 944-953.
  • Oh, S., N. An, Y. Cho, M. Jung, and Kwanguk Kim. “When LLMs recognize your space: Research on experiences with spatially aware LLM agents.” IEEE Transactions on Visualization and Computer Graphics (2025).
  • Cho, Y., M. Jung, J. Bae, and Kwanguk Kim. “Optimizing data collection for bodily emotion recognition: A comparative study.” IEEE Access 13 (2025): 198762-198777.
  • Kim, J., M. Kang, J. Kim, and Kwanguk Kim. “Becoming a rapid shooter in a game using embodied electrical muscle stimulation: Development and usability study.” JMIR Serious Games 13 (2025): e69330.
  • Park, G., J. Yi, T. J. Rhee, Kwanguk Kim, and Y. Lee. “TouchWalker: Real-time avatar locomotion from touchscreen finger walking.” In Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 582-591). IEEE, 2025.
  • You, J., M. Jung, and Kwanguk Kim. “Can people's brains synchronize during remote AR collaboration?.” IEEE Transactions on Visualization and Computer Graphics 31, no. 10 (2025): 7078-7088.
  • You, J., M. Jung, Y. Shin, and Kwanguk Kim. “Teacher-student inter-brain and behavioral synchronies in remote education.” IEEE Access 13 (2025): 87026-87035.
  • Ko, J., D. Kim, S. Lee, G. Wang, and Tae Hyun Kim. “Diffusion-based sRGB real noise generation via prompt-driven noise representation learning.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2026.
  • Kim, D., Y. Kim, Y. J. Oh, and Tae Hyun Kim. “UCMNet: Uncertainty-aware context memory network for under-display camera image restoration.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2026.
  • Kim, H., D. Kim, E. Jin, and Tae Hyun Kim. “Continuous degradation modeling via latent flow matching for real-world super-resolution.” In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press. 2026.
  • Kim, D., J. Ko, M. K. Ali, and Tae Hyun Kim. “IDF: Iterative dynamic filtering networks for generalizable image denoising.” In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2025.
  • Shin, J., S. Chung, Y. Yang, and Tae Hyun Kim. “HazeFlow: Revisit haze physical model as ODE and non-homogeneous haze generation for real-world dehazing.” In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 6263-6272). IEEE, 2025.
  • Lee, I., J. Park, S. Lee, Tae Hyun Kim, J. Seo, H. Lee, and Y. Park. “Efficient image super-resolution using dynamic quality control with recursive model structures.” IEEE Access 13 (2025).
  • Jung, D., D. Kim, G. Wang, and Tae Hyun Kim. “Exposure-slot: Exposure-centric representations learning with slot-in-slot attention for region-aware exposure correction.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 17892-17901). IEEE, 2025.
  • Kim, S., and Jong-Il Park. “Reducing cybersickness for 2D VR spectators using a gaze-based stabilized third-person view.” In 2026 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (pp. 1153-1154), 2026.
  • Paek, S., W. Cho, J. Jeong, and Jong-Il Park. “Patch-wise analysis and reconstruction of large-scale floorplan for digital twin modeling.” IEEE Access 13 (2025): 215031-215044.
  • Nam, H., K. Lee, Jong-Il Park, and K. Kim. “AR Fitness Dog: Effects of a user-mimicking interactive virtual pet on user experience and social presence in physical exercise.” IEEE Transactions on Visualization and Computer Graphics, 31(5) (2025).
  • Kim, M., and Yoonsang Lee. “FreeMusco: Motion-free learning of latent control for morphology-adaptive locomotion in musculoskeletal characters.” In SIGGRAPH Asia 2025 Conference Papers (Article 38, pp. 1-11). ACM, 2025.
  • Jang, B., E. Jung, and Yoonsang Lee. “B2F: End-to-end body-to-face motion generation with style reference.” In Pacific Graphics 2025, 2025.
  • Park, G., J. Yi, T. J. Rhee, K. Kim, and Yoonsang Lee. “TouchWalker: Real-time avatar locomotion from touchscreen finger walking.” In Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 582-591). IEEE, 2025.
  • Kim, M., E. Jung, and Yoonsang Lee. “PhysicsFC: Learning user-controlled skills for a physics-based football player controller.” ACM Transactions on Graphics 44, no. 4 (2025): Article 130, 1-21.
  • Li, X., K.-J. Oh, J. Oh, G. Lee, and Euee S. Jang. “A media type conversion framework between 3D object and MIV.” SoftwareX 29 (2026): 102597.
  • Dong, T., S. Cook, J. Oh, and Euee S. Jang. “Evaluation and analysis of image compression effect on neural network-based heart rate classification.” Scientific Reports 15 (2025): 23173.
  • Li, X., E.-Y. Chang, J. Cha, and Euee S. Jang. “Efficient inter-prediction method using reference frame accumulation for MPEG G-PCC.” IEEE Access 13 (2025).
  • Oh, J., K.-J. Oh, X. Li, G. Lee, and Euee S. Jang. “Separated background and foreground coding for MPEG immersive video coding.” IEEE Access 13 (2025): 186785-186794.
  • Oh, J., X. Li, K.-J. Oh, G. Lee, and Euee S. Jang. “Efficient atlas coding strategy using cropping for object-based MPEG immersive video.” In Proceedings of the International Conference on Advanced Communications Technology (ICACT) (pp. 253-260). IEEE, 2025.
  • Lee, Y., J. Ryu, D. Yoon, and Donghyeon Cho. “GPGS: Consistent 3D object removal via geometry-aware 3D inpainting and projected image refinement in 3D Gaussian splatting.” Proceedings of the AAAI Conference on Artificial Intelligence 40, no. 8 (2026): 5927-5935.
  • Song, D.-Y., J.-J. Yu, and Donghyeon Cho. “Progressive artwork outpainting via latent diffusion models.” In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 15405-15415). IEEE, 2025.
  • Jeong, D., T. Kim, Donghyeon Cho, and S.-W. Kim. “MELON: Learning multi-aspect modality preferences for accurate multimedia recommendation.” In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (pp. 1850-1859). ACM, 2025.
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