Abstract
In-Domain GAN Inversion for Real Image Editing
Recent work has shown that X when X . However, it is difficult X . A common practice of X . However, X . As a result, X . To solve this problem, we propose an X , which not only X but also X . We first X. We then X. Extensive experiments suggest that X.
Navigating the GAN Parameter Space for Semantic Image Editing
X are currently an indispensable tool for X . Furthermore, X are especially useful for X. By gradually X, one can X.
In this paper, we significantly X. In contrast to existing works, which X, we discover X. By several simple methods, we explore X that X, which are X. The discovered X cannot X and can X. We release our code and models and hope they will serve as a handy tool for further efforts on X.
Semantically Multi-modal Image Synthesis
In this paper, we focus on X. Previous work X. We instead propose a novel X that X. Consequently,, X. Experiments on several challenging datasets demonstrate the superiority of X . We also show that X.
Introduction
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
In this paper we tackle the problem of X. Specifically, X. For example, X. While X, Therefore, X.
First, X, which is X. For example, X. In this case, X. Further, X. Our first main idea is X, our proposed X.
Second, we believe that X . Recent architectures have demonstrated that X. However, X. To alleviate this shortcoming, our second main idea is X. An important aspect of this work is that X.
Empirically, we provide an extensive evaluation of our method on several challenging datasets: X. Quantitatively, we evaluate our work on a wide range of metrics including X; qualitatively, X. Our experimental results demonstrate a large improvement over the current state-of-the-art methods. In summary, we introduce a new architecture X that has the following advantages:
Semantically Multi-modal Image Synthesis
X, has many real-world applications and draws much attention from the community. Previous works utilized different strategies for the task: X. While these methods made exceptional achievements in improving image quality and extending more applications, we take a step further to particularly focus on a specific X that X.
Related Works
Content
Experiments
Conclusion、
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
We propose X. Our main idea is X. We introduce a X. We evaluate X. While X , we also X. In summary, X. X also pushes the frontier of X. In future work, we plan to X.
