仅用10张图就能精准修改3D人脸属性，且保持身份一致。
Efficient Few-shot Identity Preserving Attribute Editing for 3D-aware Deep Generative Models
- 利用少量标注图像定位隐空间编辑方向，实现高效属性修改。
- 仅需10张以内样本即可完成3D人脸光照、眼镜、表情等编辑。
- 适合需要高保真3D人脸编辑的数字人、影视特效等场景。
身份保持的人脸编辑任务可在不改变面部身份的前提下，调整光照、增减眼镜、改变发型或表情等。尽管2D生成模型已能通过GAN的组合性实现逼真编辑，但3D人脸编辑仍具挑战：生成模型需处理多视角一致性并渲染真实感3D人脸。此外，3D肖像编辑依赖大规模属性标注数据集，且在低分辨率下可编辑性与高分辨率下的灵活性之间存在权衡。本文提出一种方法，基于3D感知生成模型与2D人脸编辑技术，实现高效少样本的身份保持属性编辑。实验表明，仅需10张或更少带属性标注的图像，即可估计出对应3D感知属性编辑的隐空间方向。研究利用已有带掩码的人脸数据集生成少量所需属性样本，并通过顺序编辑验证编辑线性性，结合(2D)属性风格操控(ASM)技术探索3D一致的身份保持人脸老化连续风格流形。代码与结果见：https://vishal-vinod.github.io/gmpi-edit/
原文摘要 · Abstract (English)
Identity preserving editing of faces is a generative task that enables modifying the illumination, adding/removing eyeglasses, face aging, editing hairstyles, modifying expression etc., while preserving the identity of the face. Recent progress in 2D generative models have enabled photorealistic editing of faces using simple techniques leveraging the compositionality in GANs. However, identity preserving editing for 3D faces with a given set of attributes is a challenging task as the generative model must reason about view consistency from multiple poses and render a realistic 3D face. Further, 3D portrait editing requires large-scale attribute labelled datasets and presents a trade-off between editability in low-resolution and inflexibility to editing in high resolution. In this work, we aim to alleviate some of the constraints in editing 3D faces by identifying latent space directions that correspond to photorealistic edits. To address this, we present a method that builds on recent advancements in 3D-aware deep generative models and 2D portrait editing techniques to perform efficient few-shot identity preserving attribute editing for 3D-aware generative models. We aim to show from experimental results that using just ten or fewer labelled images of an attribute is sufficient to estimate edit directions in the latent space that correspond to 3D-aware attribute editing. In this work, we leverage an existing face dataset with masks to obtain the synthetic images for few attribute examples required for estimating the edit directions. Further, to demonstrate the linearity of edits, we investigate one-shot stylization by performing sequential editing and use the (2D) Attribute Style Manipulation (ASM) technique to investigate a continuous style manifold for 3D consistent identity preserving face aging. Code and results are available at: https://vishal-vinod.github.io/gmpi-edit/
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