TriPlaneNet: An Encoder for EG3D Inversion

• URL: http://arxiv.org/abs/2303.13497v2
• Date: Wed, 25 Oct 2023 19:22:30 GMT
• Title: TriPlaneNet: An Encoder for EG3D Inversion
• Authors: Ananta R. Bhattarai, Matthias Nie{\ss}ner, Artem Sevastopolsky
• Abstract summary: NeRF-based GANs have introduced a number of approaches for high-resolution and high-fidelity generative modeling of human heads. Despite the success of universal optimization-based methods for 2D GAN inversion, those applied to 3D GANs may fail to extrapolate the result onto the novel view. We introduce a fast technique that bridges the gap between the two approaches by directly utilizing the tri-plane representation presented for the EG3D generative model.
• Score: 1.9567015559455132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent progress in NeRF-based GANs has introduced a number of approaches for high-resolution and high-fidelity generative modeling of human heads with a possibility for novel view rendering. At the same time, one must solve an inverse problem to be able to re-render or modify an existing image or video. Despite the success of universal optimization-based methods for 2D GAN inversion, those applied to 3D GANs may fail to extrapolate the result onto the novel view, whereas optimization-based 3D GAN inversion methods are time-consuming and can require at least several minutes per image. Fast encoder-based techniques, such as those developed for StyleGAN, may also be less appealing due to the lack of identity preservation. Our work introduces a fast technique that bridges the gap between the two approaches by directly utilizing the tri-plane representation presented for the EG3D generative model. In particular, we build upon a feed-forward convolutional encoder for the latent code and extend it with a fully-convolutional predictor of tri-plane numerical offsets. The renderings are similar in quality to the ones produced by optimization-based techniques and outperform the ones by encoder-based methods. As we empirically prove, this is a consequence of directly operating in the tri-plane space, not in the GAN parameter space, while making use of an encoder-based trainable approach. Finally, we demonstrate significantly more correct embedding of a face image in 3D than for all the baselines, further strengthened by a probably symmetric prior enabled during training.

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