Towards harmonized regional style transfer and manipulation for facial images

Abstract3D-aware image synthesis has attained high quality and robust 3D consistency. Existing 3D controllable generative models are designed to synthesize 3D-aware images through a single modality, such as 2D segmentation or sketches, but lack the ability to finely control generated content, such as texture and age. In pursuit of enhancing user-guided controllability, we propose Multi3D, a 3D-aware controllable image synthesis model that supports multi-modal input. Our model can govern the geometry of the generated image using a 2D label map, such as a segmentation or sketch map, while concurrently regulating the appearance of the generated image through a textual description. To demonstrate the effectiveness of our method, we have conducted experiments on multiple datasets, including CelebAMask-HQ, AFHQ-cat, and shapenet-car. Qualitative and quantitative evaluations show that our method outperforms existing state-of-the-art methods.

Section: D-aware Image Synthesismentioning

confidence: 99%

Multi3D: 3D-aware multimodal image synthesis

Zhou,

Yuan,

2024

Controllable multi-domain semantic artwork synthesis

Huang,

Iizuka,

Simo-Serra

et al. 2024

We present a novel framework for the multi-domain synthesis of artworks from semantic layouts. One of the main limitations of this challenging task is the lack of publicly available segmentation datasets for art synthesis. To address this problem, we propose a dataset called ArtSem that contains 40,000 images of artwork from four different domains, with their corresponding semantic label maps. We first extracted semantic maps from landscape photography and used a conditional generative adversarial network (GAN)-based approach for generating high-quality artwork from semantic maps without requiring paired training data. Furthermore, we propose an artwork-synthesis model using domain-dependent variational encoders for high-quality multi-domain synthesis. Subsequently, the model was improved and complemented with a simple but effective normalization method based on jointly normalizing semantics and style, which we call spatially style-adaptive normalization (SSTAN). Compared to the previous methods, which only take semantic layout as the input, our model jointly learns style and semantic information representation, improving the generation quality of artistic images. These results indicate that our model learned to separate the domains in the latent space. Thus, we can perform fine-grained control of the synthesized artwork by identifying hyperplanes that separate the different domains. Moreover, by combining the proposed dataset and approach, we generated user-controllable artworks of higher quality than that of existing approaches, as corroborated by quantitative metrics and a user study.

Audio-guided implicit neural representation for local image stylization

Lee,

Kim,

Byeon

et al. 2024

We present a novel framework for audio-guided localized image stylization. Sound often provides information about the specific context of a scene and is closely related to a certain part of the scene or object. However, existing image stylization works have focused on stylizing the entire image using an image or text input. Stylizing a particular part of the image based on audio input is natural but challenging. This work proposes a framework in which a user provides an audio input to localize the target in the input image and another to locally stylize the target object or scene. We first produce a fine localization map using an audio-visual localization network leveraging CLIP embedding space. We then utilize an implicit neural representation (INR) along with the predicted localization map to stylize the target based on sound information. The INR manipulates local pixel values to be semantically consistent with the provided audio input. Our experiments show that the proposed framework outperforms other audio-guided stylization methods. Moreover, we observe that our method constructs concise localization maps and naturally manipulates the target object or scene in accordance with the given audio input.