You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image
This addresses the problem of generating accurate novel views from low-quality face images for applications like face recognition or virtual reality, representing a novel method for a known bottleneck.
The paper tackles novel-view synthesis from a single degraded face image by proposing a one-stage method that directly generates consistent novel views, outperforming traditional two-stage approaches in consistency and fidelity.
We propose a novel one-stage method, NVB-Face, for generating consistent Novel-View images directly from a single Blind Face image. Existing approaches to novel-view synthesis for objects or faces typically require a high-resolution RGB image as input. When dealing with degraded images, the conventional pipeline follows a two-stage process: first restoring the image to high resolution, then synthesizing novel views from the restored result. However, this approach is highly dependent on the quality of the restored image, often leading to inaccuracies and inconsistencies in the final output. To address this limitation, we extract single-view features directly from the blind face image and introduce a feature manipulator that transforms these features into 3D-aware, multi-view latent representations. Leveraging the powerful generative capacity of a diffusion model, our framework synthesizes high-quality, consistent novel-view face images. Experimental results show that our method significantly outperforms traditional two-stage approaches in both consistency and fidelity.