ManiCLIP: Multi-Attribute Face Manipulation from Text
This addresses the challenge of precise, multi-attribute facial image editing for applications in digital media and entertainment, representing an incremental improvement over existing single-attribute or optimization-heavy methods.
The paper tackles the problem of multi-attribute face manipulation from text, which often leads to excessive changes in both relevant and irrelevant attributes, and proposes a decoupling training scheme with group sampling and entropy constraints to achieve natural editing without test-time optimization.
In this paper we present a novel multi-attribute face manipulation method based on textual descriptions. Previous text-based image editing methods either require test-time optimization for each individual image or are restricted to single attribute editing. Extending these methods to multi-attribute face image editing scenarios will introduce undesired excessive attribute change, e.g., text-relevant attributes are overly manipulated and text-irrelevant attributes are also changed. In order to address these challenges and achieve natural editing over multiple face attributes, we propose a new decoupling training scheme where we use group sampling to get text segments from same attribute categories, instead of whole complex sentences. Further, to preserve other existing face attributes, we encourage the model to edit the latent code of each attribute separately via an entropy constraint. During the inference phase, our model is able to edit new face images without any test-time optimization, even from complex textual prompts. We show extensive experiments and analysis to demonstrate the efficacy of our method, which generates natural manipulated faces with minimal text-irrelevant attribute editing. Code and pre-trained model are available at https://github.com/hwang1996/ManiCLIP.