HyperInverter: Improving StyleGAN Inversion via Hypernetwork proposes a novel strategy for GAN inversion with an encoder mapping input images to StyleGAN2’s \(\mathcal{W}\)-space, complemented by hypernetworks to enhance reconstruction accuracy. This approach fulfills high reconstruction quality, editability, and fast inference, thereby significantly improving real-world image manipulation.

• The presented method utilizes encoder-based hypernetworks for GAN inversion.
• Achieves high-quality reconstruction due to hypernetwork branch.
• Maintains excellent editability through inversion in \(\mathcal{W}\)-space.
• Allows for extremely fast inference, vital for practical applications.
• Outperforms existing models on challenging datasets verifying its superiority.

The importance of this work lies in its innovative combination of hypernetworks and latent space mapping. The increased speed and quality of inversion process this method provides could revolutionize industrial applications of image manipulation, making the technique accessibly swift for real-time editing and opening up new creative avenues for designers and artists.