Arxiv paper - FaceLift: Single Image to 3D Head with View Generation and GS-LRM

In this episode, we discuss FaceLift: Single Image to 3D Head with View Generation and GS-LRM by Weijie Lyu, Yi Zhou, Ming-Hsuan Yang, Zhixin Shu. FaceLift is a feed-forward approach for rapid and high-quality 360-degree head reconstruction using a single image, utilizing a multi-view latent diffusion model followed by a GS-LRM reconstructor to create 3D representations from generated views. It is trained primarily on synthetic datasets, showing strong real-world generalization, and outperforms existing 3D head reconstruction methods. Additionally, FaceLift enables 4D novel view synthesis for video inputs and can be integrated with 2D reanimation techniques for 3D facial animation.