Towards Arbitrary Text-driven Image Manipulation via Space Alignment
The recent GAN inversion methods have been able to successfully invert the real image input to the corresponding editable latent code in StyleGAN. By combining with the language-vision model (CLIP), some text-driven image manipulation methods are proposed. However, these methods require extra costs to perform optimization for a certain image or a new attribute editing mode. To achieve a more efficient editing method, we propose a new Text-driven image Manipulation framework via Space Alignment (TMSA). The Space Alignment module aims to align the same semantic regions in CLIP and StyleGAN spaces. Then, the text input can be directly accessed into the StyleGAN space and be used to find the semantic shift according to the text description. The framework can support arbitrary image editing mode without additional cost. Our work provides the user with an interface to control the attributes of a given image according to text input and get the result in real time. Ex tensive experiments demonstrate our superior performance over prior works.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeImage ManipulationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real ima…
Image ManipulationOne Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations
Free-form text prompts allow users to describe their intentions during image manipulation conveniently. Based on the visual latent space of StyleGAN[21] and text embedding space of CLIP[34], studies focus on how to map t…
AllAttributeFormImage ManipulationText-conditional Attribute Alignment across Latent Spaces for 3D Controllable Face Image Synthesis
With the advent of generative models and vision language pretraining significant improvement has been made in text-driven face manipulation. The text embedding can be used as target supervision for expression control…
AttributeImage GenerationBridging CLIP and StyleGAN through Latent Alignment for Image Editing
Text-driven image manipulation is developed since the vision-language model (CLIP) has been proposed. Previous work has adopted CLIP to design a text-image consistency-based objective to address this issue. However, thes…
Image GenerationImage ManipulationLanguage ModelingLanguage Modelling+23D Guided Fine-Grained Face Manipulation
We present a method for fine-grained face manipulation. Given a face image with an arbitrary expression, our method can synthesize another arbitrary expression by the same person. This is achieved by first fitting a 3D f…
Face Model