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3D-Aware Image Synthesis via Local Spatial Self-Attention
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Chenggang Song1), Xia Yuan1), Tao Wu1), Zhipeng Yang2), Wei Liu3), Ying Fu1), *
Journal of Computer-Aided Design & Computer Graphics | 2025, 37(12) : 2061 - 2076
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Journal of Computer-Aided Design & Computer Graphics | 2025, 37(12): 2061-2076
3D-Aware Image Synthesis via Local Spatial Self-Attention
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Chenggang Song1), Xia Yuan1), Tao Wu1), Zhipeng Yang2), Wei Liu3), Ying Fu1), *
Affiliations
  • 1)School of Computer Science, Chengdu University of Information Technology, Chengdu 610225
  • 2)College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225
  • 3)Department of Automation, Shanghai Jiao Tong University, Shanghai 200240
Published: 2025-12-20 doi: 10.3724/SP.J.1089.2024-00073
Outline
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The neural radiance field (NeRF), endowed with the implicit representation capability to model 3D scenes as continuous functions, facilitates the generation model in accomplishing 3D-aware image synthesis tasks on unstructured 2D image sets. Nevertheless, current approaches face challenges in image synthesis, including low fidelity, poor consistency and uncontrollability, stemming from their inability to fully capture the three-dimensional essence of the real world or their reliance on insufficiently expressive network architectures. To address these limitations, we introduce a locally spatial neural representation method grounded in a self-attention mechanism. This approach augments the NeRF generator, empowering it to achieve controllable high-quality 3D-aware image synthesis. Initially, the scene to be synthesized is decomposed by incorporating the pose information of each object and the predefined 3D boundary box. Subsequently, the self-attention local representation module models each object distinctly. A defined combination operator is then utilized to obtain the complete scene representation. Finally, the rendering module processes the resulting RGB image. Experimental results show that the proposed method accomplishes the task of high-quality 3D-aware image synthesis with multi-view consistency and controllability, even under conditions of low-frequency spatial point sampling. Furthermore, when conducting synthesis testing with resolutions of 64×64 and 256×256 pixels on the highly complex CompCars public dataset, our proposed method achieved a 4.69 and 2.51 improvement in terms of FID scores compared to the baseline method, respectively. Similarly, on the Churches public dataset, the FID scores were improved by 1.93 and 3.56, respectively, further highlighting the effectiveness of the method.

neural radiance field  /  implicit representation  /  attention mechanism  /  3D-aware image synthesis
宋成刚, 袁霞, 吴涛, 杨智鹏, 刘伟, 符颖. 基于局部空间自注意力的3D感知图像合成. 计算机辅助设计与图形学学报, 2025 , 37 (12) : 2061 -2076 . DOI: 10.3724/SP.J.1089.2024-00073
Chenggang Song, Xia Yuan, Tao Wu, Zhipeng Yang, Wei Liu, Ying Fu. 3D-Aware Image Synthesis via Local Spatial Self-Attention[J]. Journal of Computer-Aided Design & Computer Graphics, 2025 , 37 (12) : 2061 -2076 . DOI: 10.3724/SP.J.1089.2024-00073
Year 2025 volume 37 Issue 12
81
10
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Article Info
doi: 10.3724/SP.J.1089.2024-00073
  • Receive Date:2024-02-02
  • Online Date:2026-10-10
  • Published:2025-12-20
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History
  • Received:2024-02-02
  • Revised:2024-09-29
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Affiliations
    1)School of Computer Science, Chengdu University of Information Technology, Chengdu 610225
    2)College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225
    3)Department of Automation, Shanghai Jiao Tong University, Shanghai 200240
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https://castjournals.cast.org.cn/joweb/jsjfzsj/EN/10.3724/SP.J.1089.2024-00073
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表12种不同金属材料的力学参数
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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