单张模糊人脸图直接生成一致的多视角图像。
You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image
- 从单张模糊人脸直接提取特征，一步生成多视角图像。
- 在一致性与保真度上显著优于传统两阶段方法。
- 适合处理低质量输入，对修复效果依赖更小。
我们提出一种新型单阶段方法 NVB-Face，可直接从单张盲人脸图像生成一致的多视角图像。现有物体或人脸的多视角合成方法通常需要高分辨率RGB图像作为输入。当处理退化图像时，传统流程分为两步：先恢复至高分辨率，再基于恢复结果生成新视角。然而该方法严重依赖恢复图像质量，常导致最终输出不准确且不一致。为解决此问题，我们直接从盲人脸图像中提取单视图特征，并引入特征转换器，将这些特征转化为具有3D感知能力的多视角潜在表示。利用扩散模型强大的生成能力，我们的框架能够合成高质量、一致的多视角人脸图像。实验表明，该方法在一致性与保真度上显著优于传统两阶段方法。
原文摘要 · Abstract (English)
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.
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