Talk-to-Edit: Fine-Grained Facial Editing via Dialog

• URL: http://arxiv.org/abs/2109.04425v1
• Date: Thu, 9 Sep 2021 17:17:59 GMT
• Title: Talk-to-Edit: Fine-Grained Facial Editing via Dialog
• Authors: Yuming Jiang, Ziqi Huang, Xingang Pan, Chen Change Loy, Ziwei Liu
• Abstract summary: Talk-to-Edit is an interactive facial editing framework that performs fine-grained attribute manipulation through dialog between the user and the system. Our key insight is to model a continual "semantic field" in the GAN latent space. Our system generates language feedback by considering both the user request and the current state of the semantic field.
• Score: 79.8726256912376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial editing is an important task in vision and graphics with numerous applications. However, existing works are incapable to deliver a continuous and fine-grained editing mode (e.g., editing a slightly smiling face to a big laughing one) with natural interactions with users. In this work, we propose Talk-to-Edit, an interactive facial editing framework that performs fine-grained attribute manipulation through dialog between the user and the system. Our key insight is to model a continual "semantic field" in the GAN latent space. 1) Unlike previous works that regard the editing as traversing straight lines in the latent space, here the fine-grained editing is formulated as finding a curving trajectory that respects fine-grained attribute landscape on the semantic field. 2) The curvature at each step is location-specific and determined by the input image as well as the users' language requests. 3) To engage the users in a meaningful dialog, our system generates language feedback by considering both the user request and the current state of the semantic field. We also contribute CelebA-Dialog, a visual-language facial editing dataset to facilitate large-scale study. Specifically, each image has manually annotated fine-grained attribute annotations as well as template-based textual descriptions in natural language. Extensive quantitative and qualitative experiments demonstrate the superiority of our framework in terms of 1) the smoothness of fine-grained editing, 2) the identity/attribute preservation, and 3) the visual photorealism and dialog fluency. Notably, user study validates that our overall system is consistently favored by around 80% of the participants. Our project page is https://www.mmlab-ntu.com/project/talkedit/.

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