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TG-TSGNet: A Text-Guided Arbitrary-Resolution Terrain Scene Generation Network.

Yifan Zhu, Yan Wang, Xinghui Dong

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 19, 2025
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    Summary
    This summary is machine-generated.

    This study introduces a new network for generating realistic terrain scenes from text descriptions, improving efficiency and semantic consistency in visualization applications like AR and VR.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Artificial Intelligence

    Background:

    • Traditional terrain modeling struggles with efficiency, realism, and semantic consistency for applications like AR, VR, and geographic mapping.
    • There is a growing demand for advanced terrain visualization techniques.

    Purpose of the Study:

    • To propose a novel Text-Guided Arbitrary-Resolution Terrain Scene Generation Network (TG-TSGNet) to overcome limitations in current terrain modeling.
    • To enhance the efficiency, realism, and semantic consistency of terrain scene generation.

    Main Methods:

    • Developed TG-TSGNet, integrating a ConvMamba-VQGAN with custom Conv-Based Local Representation Blocks (CLRB) and Mamba-Based Global Representation Blocks (MGRB).
    • Incorporated a Text Guidance Sub-network with a text encoder and Text-Image Alignment Module (TIAM) for semantic integration.
    • Included an Arbitrary-Resolution Image Super-Resolution Module (ARSRM) trained in conjunction with the VQGAN.
    • Generated textual descriptions for the Natural Terrain Scene Data Set (NTSD) comprising 36,672 images across 38 categories.

    Main Results:

    • TG-TSGNet demonstrated superior or comparable performance to baseline methods in image realism and semantic consistency.
    • The network effectively captures both local and global terrain characteristics and semantics.
    • Achieved reduced computational cost in image generation.

    Conclusions:

    • TG-TSGNet offers a significant advancement in text-guided terrain scene generation.
    • The proposed architecture effectively balances realism, semantic consistency, and computational efficiency.
    • The model is well-suited for demanding visualization applications requiring high-quality terrain data.