Language-Driven Synthesis of 3D Scenes from Scene Databases

Rui Ma* 1,2 Akshay Gadi Patil* 1 Matthew Fisher 3 Manyi Li 4,1 Sören Pirk 5
Binh-Son Hua 6 Sai-Kit Yeung 7 Xin Tong 8 Leonidas Guibas 5 Hao Zhang 1
1 Simon Fraser University 2 AltumView Systems Inc. 3 Adobe Research
4 Shandong University 5 Stanford University 6 University of Tokyo
7 Hong Kong University of Science and Technology 8 Microsoft Research Asia




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Abstract

We introduce a novel framework for using natural language to generate and edit 3D indoor scenes, harnessing scene semantics and text-scene grounding knowledge learned from large annotated 3D scene databases. The advantage of natural language editing interfaces is strongest when performing semantic operations at the sub-scene level, acting on groups of objects. We learn how to manipulate these sub-scenes by analyzing existing 3D scenes.We perform edits by first parsing a natural language command from the user and transforming it into a semantic scene graph that is used to retrieve corresponding sub-scenes from the databases that match the command. We then augment this retrieved sub-scene by incorporating other objects that may be implied by the scene context. Finally, a new 3D scene is synthesized by aligning the augmented sub-scene with the user's current scene, where new objects are spliced into the environment, possibly triggering appropriate adjustments to the existing scene arrangement. A suggestive modeling interface with multiple interpretations of user commands is used to alleviate ambiguities in natural language. We conduct studies comparing our approach against both prior text-to-scene work and artist-made scenes and find that our method significantly outperforms prior work and is comparable to handmade scenes even when complex and varied natural sentences are used.

Paper [Coming Soon] Code [To be Released] Dataset [To be Released]

Please note that we cannot publicly release the SUNCG dataset as it is licensed. To access this data, please refer toSUNCG website.

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Bibtex

If you find this work useful for your research, please cite our paper, using the Bibtex below:

@article{ma2018language,
title={Language-Driven Synthesis of 3D Scenes from Scene Databases},
author={Ma, Rui and Gadi Patil, Akshay and Fisher, Matthew and Li, Manyi and Pirk, Sören and Hua, Binh-Son and Yeung, Sai-Kit and Tong, Xin and Guibas, Leonidas and Zhang, Hao},
journal={ACM Transactions on Graphics (TOG)},
volume={37},
number={6},
year={2018},
publisher={ACM}
}

Acknowledgment

We thank the anonymous reviewers for their valuable comments.This work was supported, in parts, by an NSERC grant (611370), an NSF grant IIS-1528025, the Stanford AI Lab-Toyota Center for Artificial Intelligence Research, the Singapore MOE Academic Research Fund MOE2016-T2-2-154, an internal grant from HKUST (R9429), and gift funds from Adobe and Amazon AWS. We also thank Phuchong Yamchomsuan for creating the artist scenes, as well as Quang-Hieu Pham and Chenyang Zhu for helping with pre-processing the scene databases.

* Co-First Authors