Real-time 3D-aware Portrait Editing from a Single Image

• URL: http://arxiv.org/abs/2402.14000v3
• Date: Thu, 18 Jul 2024 13:43:41 GMT
• Title: Real-time 3D-aware Portrait Editing from a Single Image
• Authors: Qingyan Bai, Zifan Shi, Yinghao Xu, Hao Ouyang, Qiuyu Wang, Ceyuan Yang, Xuan Wang, Gordon Wetzstein, Yujun Shen, Qifeng Chen,
• Abstract summary: 3DPE can edit a face image following given prompts, like reference images or text descriptions. A lightweight module is distilled from a 3D portrait generator and a text-to-image model.
• Score: 111.27169315556444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents 3DPE, a practical method that can efficiently edit a face image following given prompts, like reference images or text descriptions, in a 3D-aware manner. To this end, a lightweight module is distilled from a 3D portrait generator and a text-to-image model, which provide prior knowledge of face geometry and superior editing capability, respectively. Such a design brings two compelling advantages over existing approaches. First, our method achieves real-time editing with a feedforward network (i.e., ~0.04s per image), over 100x faster than the second competitor. Second, thanks to the powerful priors, our module could focus on the learning of editing-related variations, such that it manages to handle various types of editing simultaneously in the training phase and further supports fast adaptation to user-specified customized types of editing during inference (e.g., with ~5min fine-tuning per style).

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