🔥🔥 PanoHead: A 3D-Aware Generative Model for High-Quality View-Consistent Image Synthesis of Full Heads

#paperwithme #artificialintelligence #machinelearning #ml #ai #deeplearning #computerscience #PanoHead #computervision

𝐌𝐨𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧:
Creating high-quality, view-consistent images of full heads in 360 degrees presents a unique set of challenges. PanoHead, a 3D-aware generative model proposed in this paper, addresses these challenges using only in-the-wild unstructured images for training. It leverages a novel two-stage self-adaptive image alignment for robust 3D GAN training, a tri-grid neural volume representation, and a foreground-aware tri-discriminator.

Explore the Project, Paper here 👇
https://sizhean.github.io/panohead

𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬:
✅ Two-Stage Self-Adaptive Image Alignment: This feature ensures robust 3D GAN training.
✅ Tri-Grid Neural Volume Representation: This effectively addresses front-face and back-head feature entanglement.
✅ Foreground-Aware Tri-Discriminator: This disentangles 3D foreground head modeling from 2D background synthesis.
✅ High-Quality 3D Heads: The model generates high-quality 3D heads with accurate geometry and diverse appearances, even with long wavy and afro hairstyles, renderable from arbitrary poses.

𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐃𝐨𝐦𝐚𝐢𝐧𝐬 𝐒𝐮𝐜𝐡 𝐀𝐬:
✅ High-quality image synthesis
✅ 3D avatar creation
✅ Personalized realistic 3D avatars

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