Talk-to-Edit: Fine-Grained 2D and 3D Facial Editing via Dialog

Facial editing is to manipulate the facial attributes of a given face image. Nowadays, with the development of generative models, users can easily generate 2D and 3D facial images with high fidelity and 3D-aware consistency. However, existing works are incapable of delivering 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 demonstrate the effectiveness of our proposed framework on both 2D and 3D-aware generative models. We term the semantic field for the 3D-aware models as "tri-plane" flow, as it corresponds to the changes not only in the color space but also in the density space. 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, the user study validates that our overall system is consistently favored by around 80% of the participants.