Latest Artificial Intelligence (AI) Research From Korea Open-Sources ‘Dr.3D,’ A Novel 3D GAN Domain Adaptation Method For Drawings

DEC 07, 2022

It is essential for a wide range of applications in computer graphics and vision that generative adversarial networks (GANs) learn to create realistic images. Notably, GANs enable semantically meaningful picture exploration and editing for real and synthetic images. It is not unexpected that the human face is one of the most frequently targeted image categories in computer vision and graphics by GAN algorithms. Making GANs aware of 3D geometry has recently attracted a lot of attention, creating an exciting study area for 3D GANs. By directly simulating 3D light transit between a camera and a target object, they address the problem of learning the 3D-aware distribution of real images.

In addition to enabling semantically meaningful photo synthesis and manipulation, 3D GANs also take 3D scene geometry into account. The majority of 3D GAN demonstrations so far have been limited to real-world pictures, which are precise records of real-world settings made with perspective cameras. In this study, they extend 3D GANs’ capacity to handle drawing, a new but significant visual form. Drawings substantially impact human history because they reflect both real and fictional subjects with deliberate and unintentional changes. By converting 2D GANs that have been pretrained on real-world images into drawings, a process known as domain adaptation, existing 2D GAN approaches have been expanded to handle drawings.

Figure 1: Examples of GAN inversion and semantic editing on a drawing of a person. By fine-tuning StyleNeRF and ????-GAN on portrait drawings, we perform naive domain adaptation to them for comparison. Then, utilising each 3D GAN model, we rebuild the image and its shape at various camera poses after inverting the input image in (a) using an off-the-shelf GAN inversion approach to a latent code. The findings in (b) and (c) demonstrate how naive 3D GAN adaptations are unable to handle the input drawing. On the other hand, as shown in (d) and (e), our system can correctly recreate the input image and also support semantic editing (e). Picture in (a): A Portrait of a Wedigh Family Member

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The adaption technique takes advantage of similarities between drawings and images, which enables us to apply the synthesis and editing capabilities of 2D GANs to the drawing domain. Unfortunately, as illustrated in Figure 1, using 3D GANs in the drawing domain is more difficult. Drawings have inherent geometric ambiguity regarding the topic and camera attitude, which is one of the leading causes of this challenge. Drawings with creative ambiguity emerge from artists’ purposeful or unintentional assumption of nondeterministic geometry of subjects from an imagined viewpoint that diverges from the physical one. This makes learning a 3D-aware image distribution of drawings even more challenging and makes it difficult to directly transfer previous domain adaptation techniques from 2D GANs to 3D GAN methods.

Figure 1 demonstrates that using domain adaptation to apply cutting-edge 3D GANs to drawings fails to produce accurate, 3D-consistent images. This study proposes Dr.3D, a brand-new 3D GAN domain adaption technique for portrait drawings. Dr.3D uses three solutions to resolve the fundamental geometric ambiguity in drawings. First, they provide a 3D synthesis network that is deformation-aware and capable of learning various forms for drawings. To successfully reduce the learning complexity of ambiguous 3D geometries and camera poses in drawings, they also present an alternating adaptation technique for 3D-aware picture synthesis and posture estimation. They thirdly impose geometric priors to facilitate stable domain adaptation from actual images to drawings. The resulting domain adaption technique, Dr.3D, is the first technique that makes it possible to alter and synthesize drawing images in 3D across time consistently. They thoroughly quantitatively and qualitatively evaluate Dr. 3D’s efficiency. PyTorch implementation will be soon made available on GitHub.

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