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New GAN Method for Full-Body Image Generation Proposed

InsetGAN combines multiple GANs: for the body and different body parts.

Neural networks creating photorealistic portraits or even 3D scenes is not a new concept. However, AI can more or less reliably generate faces, but full-body images are still difficult due to the variation in poses, clothes, hairstyles, and perspective. 

InsetGAN introduces a method of combining multiple GANs where one GAN generates a global canvas (for example, a human body) and other GANs, or insets, focus on different parts (like faces or hands) that can be seamlessly inserted onto the canvas.

Combining a full body GAN with a high-quality face GAN helps InsetGAN produce "plausible-looking" humans. The method concentrates on parts that often contain artifacts, such as faces or shoes, and combines them with an appropriate body.

Here are some more results:

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