Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis

Hoseok Do, EunKyung Yoo, Taehyeong Kim, Chul Lee, Jin Young Choi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 8529-8538

Abstract


While 3D-based GAN techniques have been successfully applied to render photo-realistic 3D images with a variety of attributes while preserving view consistency, there has been little research on how to fine-control 3D images without limiting to a specific category of objects of their properties. To fill such research gap, we propose a novel image manipulation model of 3D-based GAN representations for a fine-grained control of specific custom attributes. By extending the latest 3D-based GAN models (e.g., EG3D), our user-friendly quantitative manipulation model enables a fine yet normalized control of 3D manipulation of multi-attribute quantities while achieving view consistency. We validate the effectiveness of our proposed technique both qualitatively and quantitatively through various experiments.

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[bibtex]
@InProceedings{Do_2023_CVPR, author = {Do, Hoseok and Yoo, EunKyung and Kim, Taehyeong and Lee, Chul and Choi, Jin Young}, title = {Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {8529-8538} }