AHCI RESEARCH GROUP

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OUR RESEARCH

Scientific Publications

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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}

}