Variational Conditional GAN for Fine-grained Controllable Image Generation
This work addresses the challenge of exploiting hidden condition information in conditional GANs for image generation, offering an incremental improvement in controllable image synthesis.
The paper tackles the problem of generating fine-grained controllable images by proposing a variational generator framework for conditional GANs, which improves generation quality and diversity by inferring latent variables from conditional inputs, and it outperforms state-of-the-art methods in experiments.
In this paper, we propose a novel variational generator framework for conditional GANs to catch semantic details for improving the generation quality and diversity. Traditional generators in conditional GANs simply concatenate the conditional vector with the noise as the input representation, which is directly employed for upsampling operations. However, the hidden condition information is not fully exploited, especially when the input is a class label. Therefore, we introduce a variational inference into the generator to infer the posterior of latent variable only from the conditional input, which helps achieve a variable augmented representation for image generation. Qualitative and quantitative experimental results show that the proposed method outperforms the state-of-the-art approaches and achieves the realistic controllable images.