Jun Peng

Pixel synthesis is a promising research paradigm for image generation, which can well exploit pixel-wise prior knowledge for generation. However, existing methods still suffer from excessive memory footprint and computation overhead. In this paper, we propose a progressive pixel synthesis network towards efficient image generation, coined as PixelFolder. Specifically, PixelFolder formulates image generation as a progressive pixel regression problem and synthesizes images via a multi-stage structure, which can greatly reduce the overhead caused by large tensor transformations. In addition, we introduce novel pixel folding operations to further improve model efficiency while maintaining pixel-wise prior knowledge for end-to-end regression. With these innovative designs, we greatly reduce the expenditure of pixel synthesis, e.g., reducing 89% computation and 53% parameters compared with the latest pixel synthesis method CIPS. To validate our approach, we conduct extensive experiments on two benchmark datasets, namely FFHQ and LSUN Church. The experimental results show that with much less expenditure, PixelFolder obtains new state-of-the-art (SOTA) performance on two benchmark datasets, i.e., 3.77 FID and 2.45 FID on FFHQ and LSUN Church, respectively.Meanwhile, PixelFolder is also more efficient than the SOTA methods like StyleGAN2, reducing about 72% computation and 31% parameters, respectively. These results greatly validate the effectiveness of the proposed PixelFolder.

Text-to-Image (T2I) synthesis is a challenging task that aims to convert natural language descriptions to real images. It remains an open problem mainly due to the diversity of text descriptions, which poses a huge obstacle in generating vivid and relevant images. Moreover, the existing evaluation metrics in T2I synthesis are mainly used to evaluate the visual quality of the generated images, while the semantic consistency between the two modalities is often ignored. To address these issues, we present a novel Knowledge-Driven Generative Adversarial Network , termed KD-GAN, and a new evaluation system, named Pseudo Turing Test (PTT for short). Concretely, KD-GAN takes a further step in imitating the behavior of human painting, i.e. , drawing an image according to reference knowledge. The introduction of reference knowledge in KD-GAN not only improves the quality of the generated images but also enhances the semantic consistency between them and the input texts. In addition, KD-GAN can also greatly avoid some flaws against common sense during image generation, e.g. , skiing in the blue sky. The proposed PTT is an important supplement to the existing evaluation system of T2I synthesis. It includes a set of pseudo-experts of different multimedia tasks to evaluate the semantic consistency between the given texts and the generated images. To validate the proposed KD-GAN, we conducted extensive experiments on two benchmark datasets, i.e. , Caltech-UCSD Birds (CUB), and MS-COCO (COCO). The experimental results demonstrate that KD-GAN outperforms state-of-the-art methods on IS, FID, and the proposed PTT metrics. ^{1 1}The codes of KD-GAN are at [Online]. Available: https://github.com/pengjunn/KD-GAN and the codes and models of PTT are at [Online]. Available: https://github.com/pengjunn/PTT.