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arXiv 2104.11228cs.CVcs.GR
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基于3D制导的跨域无纠缠人脸操作

Cross-Domain and Disentangled Face Manipulation with 3D Guidance

City University of Hong Kong(香港城市大学);Snap Inc.(Snap公司);USC Institute for Creative Technologies(南加州大学创意技术研究所);Microsoft Cloud AI(微软云人工智能部门)

Can Wang, Menglei Chai, Mingming He, Dongdong Chen, Jing Liao

摘要:

基于三维引导的人脸图像操作由于其语义上的理解性和用户友好的可控性而被广泛应用于各种交互场景中。然而,现有的基于3D变形模型的操作方法并不直接适用于域外的人脸,如非真实感绘画,卡通肖像,甚至动物,主要是由于在建立模型的艰巨困难,为每个特定的人脸域。为了克服这一挑战,我们提出,据我们所知,第一种方法来操纵人脸在任意域使用人类3DMM。这是通过两个主要步骤实现的:1)从3DMM参数到预训练的StyleGAN 2的潜在空间嵌入的解纠缠映射,该预训练的StyleGAN 2保证对每个语义属性的解纠缠和精确控制;以及2)跨领域自适应,该跨领域自适应弥合了领域差异,并通过强制执行一致的潜在空间嵌入使人类3DMM适用于域外面部。实验和比较表明,我们的高品质的语义操作方法的优越性,在各种人脸域的所有主要的3D面部属性considerable-pose,表情,形状,面部表情和照明。此外,我们开发了一个直观的编辑界面,以支持用户友好的控制和即时反馈。我们的项目页面是www.example.com

英文摘要:

Face image manipulation via three-dimensional guidance has been widely applied in various interactive scenarios due to its semantically-meaningful understanding and user-friendly controllability. However, existing 3D-morphable-model-based manipulation methods are not directly applicable to out-of-domain faces, such as non-photorealistic paintings, cartoon portraits, or even animals, mainly due to the formidable difficulties in building the model for each specific face domain. To overcome this challenge, we propose, as far as we know, the first method to manipulate faces in arbitrary domains using human 3DMM. This is achieved through two major steps: 1) disentangled mapping from 3DMM parameters to the latent space embedding of a pre-trained StyleGAN2 that guarantees disentangled and precise controls for each semantic attribute; and 2) cross-domain adaptation that bridges domain discrepancies and makes human 3DMM applicable to out-of-domain faces by enforcing a consistent latent space embedding. Experiments and comparisons demonstrate the superiority of our high-quality semantic manipulation method on a variety of face domains with all major 3D facial attributes controllable-pose, expression, shape, albedo, and illumination. Moreover, we develop an intuitive editing interface to support user-friendly control and instant feedback. Our project page is https://cassiepython.github.io/cddfm3d/index.html

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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