Meta-Auxiliary Network for 3D GAN Inversion

• URL: http://arxiv.org/abs/2305.10884v1
• Date: Thu, 18 May 2023 11:26:27 GMT
• Title: Meta-Auxiliary Network for 3D GAN Inversion
• Authors: Bangrui Jiang, Zhenhua Guo, Yujiu Yang
• Abstract summary: In this work, we present a novel meta-auxiliary framework, while leveraging the newly developed 3D GANs as generator. In the first stage, we invert the input image to an editable latent code using off-the-shelf inversion techniques. The auxiliary network is proposed to refine the generator parameters with the given image as input, which both predicts offsets for weights of convolutional layers and sampling positions of volume rendering. In the second stage, we perform meta-learning to fast adapt the auxiliary network to the input image, then the final reconstructed image is synthesized via the meta-learned auxiliary network.
• Score: 18.777352198191004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world image manipulation has achieved fantastic progress in recent years. GAN inversion, which aims to map the real image to the latent code faithfully, is the first step in this pipeline. However, existing GAN inversion methods fail to achieve high reconstruction quality and fast inference at the same time. In addition, existing methods are built on 2D GANs and lack explicitly mechanisms to enforce multi-view consistency.In this work, we present a novel meta-auxiliary framework, while leveraging the newly developed 3D GANs as generator. The proposed method adopts a two-stage strategy. In the first stage, we invert the input image to an editable latent code using off-the-shelf inversion techniques. The auxiliary network is proposed to refine the generator parameters with the given image as input, which both predicts offsets for weights of convolutional layers and sampling positions of volume rendering. In the second stage, we perform meta-learning to fast adapt the auxiliary network to the input image, then the final reconstructed image is synthesized via the meta-learned auxiliary network. Extensive experiments show that our method achieves better performances on both inversion and editing tasks.

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