PanoHead

PanoHead is a 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in 360° with diverse appearance and detailed geometry using only in-the-wild unstructured images for training. It was proposed by Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Y. Ogras, and Linjie Luo in their paper "PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360°" published in CVPR 2023.

Synthesis and reconstruction of 3D human head has gained increasing interests in computer vision and computer graphics recently. Existing state-of-the-art 3D Generative Adversarial Networks (GANs) for 3D human head synthesis are either limited to near-frontal views or hard to preserve 3D consistency in large view angles.

PanoHead is the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in 360° with diverse appearance and detailed geometry using only in-the-wild unstructured images for training.

At its core, the model lifts up the representation power of recent 3D GANs and bridge the data alignment gap when training from in-the-wild images with widely distributed views. Specifically, a novel two-stage self-adaptive image alignment for robust 3D GAN training. The model further introduces a tri-grid neural volume representation that effectively addresses front-face and back-head feature entanglement rooted in the widely-adopted tri-plane formulation. This method instills prior knowledge of 2D image segmentation in adversarial learning of 3D neural scene structures, enabling compositable head synthesis in diverse backgrounds.

Benefiting from these designs, this method significantly outperforms previous 3D GANs, generating high-quality 3D heads with accurate geometry and diverse appearances, even with long wavy and afro hairstyles, renderable from arbitrary poses. Furthermore, PanoHead system can reconstruct full 3D heads from single input images for personalized realistic 3D avatars.

Summary:

PanoHead has several key features that make it a powerful tool for 3D head synthesis:

  • It uses a tri-grid neural volume representation that effectively addresses front-face and back-head feature entanglement.

  • It instills prior knowledge of 2D image segmentation in adversarial learning of 3D neural scene structures, enabling compositable head synthesis in diverse backgrounds.

  • It is trained on a large dataset of in-the-wild unstructured images, which allows it to generate high-quality 3D heads with accurate geometry and diverse appearances.

PanoHead has been shown to be effective in a variety of applications, including:

  • Virtual try-on for headwear and makeup

  • Avatar creation for virtual reality and augmented reality

  • Face recognition and authentication

  • Medical imaging

PanoHead is a promising new technology for 3D head synthesis. It has the potential to revolutionize the way we interact with digital content, and it is likely to have a significant impact on a wide range of industries.

Here are some links to learn more about PanoHead:

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