AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Zhang, G.; Wang, Y.; Luo, C.; Xu, S.; Ming, Y.; Peng, J.; Zhang, M.
Visual Harmony: LLM’s Power in Crafting Coherent Indoor Scenes from Images Proceedings Article
In: Z., Lin; H., Zha; M.-M., Cheng; R., He; C.-L., Liu; K., Ubul; W., Silamu; J., Zhou (Ed.): Lect. Notes Comput. Sci., pp. 3–17, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-981978507-0 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Depth perception, Indoor scene generation, Input image, Language Model, Large language model, Metaverses, Point-clouds, Power, Scene completion, Scene Generation, Scene-graphs, Semantic Segmentation, Semantics, Virtual Reality, Visual languages
@inproceedings{zhang_visual_2025,
title = {Visual Harmony: LLM’s Power in Crafting Coherent Indoor Scenes from Images},
author = {G. Zhang and Y. Wang and C. Luo and S. Xu and Y. Ming and J. Peng and M. Zhang},
editor = {Lin Z. and Zha H. and Cheng M.-M. and He R. and Liu C.-L. and Ubul K. and Silamu W. and Zhou J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209374797&doi=10.1007%2f978-981-97-8508-7_1&partnerID=40&md5=5231ab0bce95fb3f09db80392acd58ff},
doi = {10.1007/978-981-97-8508-7_1},
isbn = {03029743 (ISSN); 978-981978507-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15036 LNCS},
pages = {3–17},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Indoor scene generation has recently attracted significant attention as it is crucial for metaverse, 3D animation, visual effects in movies, and virtual/augmented reality. Existing image-based indoor scene generation methods often produce scenes that are not realistic enough, with issues such as floating objects, incorrect object orientations, and incomplete scenes that only include the part of the scenes captured by the input image. To address these challenges, we propose Visual Harmony, a method that leverages the powerful spatial imagination capabilities of Large Language Model (LLM) to generate corresponding indoor scenes based on the input image. Specifically, we first extract information from the input image through depth estimation and panorama segmentation, reconstructing a semantic point cloud. Using this reconstructed semantic point cloud, we extract a scene graph that describes only the objects in the image. Then we leverage the strong spatial imagination capabilities of LLM to complete the scene graph, forming a representation of a complete room scene. Based on this fine scene graph, we can generate entire indoor scene that includes both the captured and not captured parts of the input image. Extensive experiments demonstrate that our method can generate realistic, plausible, and highly relevant complete indoor scenes related to the input image. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.},
keywords = {Augmented Reality, Depth perception, Indoor scene generation, Input image, Language Model, Large language model, Metaverses, Point-clouds, Power, Scene completion, Scene Generation, Scene-graphs, Semantic Segmentation, Semantics, Virtual Reality, Visual languages},
pubstate = {published},
tppubtype = {inproceedings}
}
Indoor scene generation has recently attracted significant attention as it is crucial for metaverse, 3D animation, visual effects in movies, and virtual/augmented reality. Existing image-based indoor scene generation methods often produce scenes that are not realistic enough, with issues such as floating objects, incorrect object orientations, and incomplete scenes that only include the part of the scenes captured by the input image. To address these challenges, we propose Visual Harmony, a method that leverages the powerful spatial imagination capabilities of Large Language Model (LLM) to generate corresponding indoor scenes based on the input image. Specifically, we first extract information from the input image through depth estimation and panorama segmentation, reconstructing a semantic point cloud. Using this reconstructed semantic point cloud, we extract a scene graph that describes only the objects in the image. Then we leverage the strong spatial imagination capabilities of LLM to complete the scene graph, forming a representation of a complete room scene. Based on this fine scene graph, we can generate entire indoor scene that includes both the captured and not captured parts of the input image. Extensive experiments demonstrate that our method can generate realistic, plausible, and highly relevant complete indoor scenes related to the input image. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
2024
Jayaraman, S.; Bhavya, R.; Srihari, V.; Rajam, V. Mary Anita
TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions Proceedings Article
In: IEEE Int. Conf. Comput. Vis. Mach. Intell., CVMI, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037687-6 (ISBN).
Abstract | Links | BibTeX | Tags: Adversarial networks, Computer simulation languages, Deep learning, Depth Estimation, Depth perception, Diffusion Model, diffusion models, Digital elevation model, Generative adversarial networks, Generative model, Generative systems, Language Model, Motion capture, Stereo image processing, Text-to-image, Training data, Video analysis, Video-clips, Virtual environments, Virtual Reality
@inproceedings{jayaraman_texavi_2024,
title = {TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions},
author = {S. Jayaraman and R. Bhavya and V. Srihari and V. Mary Anita Rajam},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215265234&doi=10.1109%2fCVMI61877.2024.10782691&partnerID=40&md5=8e20576af67b917ecfad83873a87ef29},
doi = {10.1109/CVMI61877.2024.10782691},
isbn = {979-835037687-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Int. Conf. Comput. Vis. Mach. Intell., CVMI},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic depth and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with depth estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations. © 2024 IEEE.},
keywords = {Adversarial networks, Computer simulation languages, Deep learning, Depth Estimation, Depth perception, Diffusion Model, diffusion models, Digital elevation model, Generative adversarial networks, Generative model, Generative systems, Language Model, Motion capture, Stereo image processing, Text-to-image, Training data, Video analysis, Video-clips, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic depth and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with depth estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations. © 2024 IEEE.