Publications

@article{zhao_clear_2025,

title = {CleAR: Robust Context-Guided Generative Lighting Estimation for Mobile Augmented Reality},

author = {Y. Zhao and M. Dasari and T. Guo},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015452988&doi=10.1145%2F3749535&partnerID=40&md5=ed970d47cbf7f547555eca43b32cd7e7},

doi = {10.1145/3749535},

issn = {24749567 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},

volume = {9},

number = {3},

abstract = {High-quality environment lighting is essential for creating immersive mobile augmented reality (AR) experiences. However, achieving visually coherent estimation for mobile AR is challenging due to several key limitations in AR device sensing capabilities, including low camera FoV and limited pixel dynamic ranges. Recent advancements in generative AI, which can generate high-quality images from different types of prompts, including texts and images, present a potential solution for high-quality lighting estimation. Still, to effectively use generative image diffusion models, we must address two key limitations of content quality and slow inference. In this work, we design and implement a generative lighting estimation system called CleAR that can produce high-quality, diverse environment maps in the format of 360◦ HDR images. Specifically, we design a two-step generation pipeline guided by AR environment context data to ensure the output aligns with the physical environment’s visual context and color appearance. To improve the estimation robustness under different lighting conditions, we design a real-time refinement component to adjust lighting estimation results on AR devices. To train and test our generative models, we curate a large-scale environment lighting estimation dataset with diverse lighting conditions. Through a combination of quantitative and qualitative evaluations, we show that CleAR outperforms state-of-the-art lighting estimation methods on both estimation accuracy, latency, and robustness, and is rated by 31 participants as producing better renderings for most virtual objects. For example, CleAR achieves 51% to 56% accuracy improvement on virtual object renderings across objects of three distinctive types of materials and reflective properties. CleAR produces lighting estimates of comparable or better quality in just 3.2 seconds—over 110X faster than state-of-the-art methods. Moreover, CleAR supports real-time refinement of lighting estimation results, ensuring robust and timely updates for AR applications. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Association for Computing Machinery},

keywords = {Augmented Reality, Color computer graphics, Environment lighting, Estimation results, Generative model, High quality, Human engineering, Immersive, Lighting, Lighting conditions, Lighting estimation, Mobile augmented reality, Real-time refinement, Rendering (computer graphics), Statistical tests, Virtual objects, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{na_generative_2024,

title = {Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing},

author = {M. Na and J. Lee},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205236316&doi=10.3390%2Fapp14188419&partnerID=40&md5=bb3541cad9af6e839ba08fbfc7e88c08},

doi = {10.3390/app14188419},

issn = {20763417 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Applied Sciences (Switzerland)},

volume = {14},

number = {18},

abstract = {This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks. © 2024 Elsevier B.V., All rights reserved.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {Artificial intelligence technologies, Augmented Reality, benchmarking, Computation offloading, Edge computing, Energy Efficient, Generative adversarial networks, Generative AI, Image enhancement, Mobile augmented reality, Mobile edge computing, Multi-access edge computing, Multiaccess, Quality of Service, Resolution process, super-resolution, Superresolution, Trade off},

pubstate = {published},

tppubtype = {article}

}