MOC-GAN: Mixing Objects and Captions to Generate Realistic Images

TL;DR

MOC-GAN introduces a novel approach to generate realistic images by combining object information and captions, inferring implicit relations, and employing a cascaded attentive network for detailed image synthesis, outperforming existing methods.

Contribution

The paper proposes a new setting and model that explicitly use objects and captions, infer scene relations, and generate high-quality images with improved metrics.

Findings

Outperforms state-of-the-art on COCO dataset in Inception Score and FID

Effectively infers scene relations from captions

Generates high-quality, detailed images

Abstract

Generating images with conditional descriptions gains increasing interests in recent years. However, existing conditional inputs are suffering from either unstructured forms (captions) or limited information and expensive labeling (scene graphs). For a targeted scene, the core items, objects, are usually definite while their interactions are flexible and hard to clearly define. Thus, we introduce a more rational setting, generating a realistic image from the objects and captions. Under this setting, objects explicitly define the critical roles in the targeted images and captions implicitly describe their rich attributes and connections. Correspondingly, a MOC-GAN is proposed to mix the inputs of two modalities to generate realistic images. It firstly infers the implicit relations between object pairs from the captions to build a hidden-state scene graph. So a multi-layer representation containing objects, relations and captions is constructed, where the scene graph provides the structures of the scene and the caption provides the image-level guidance. Then a cascaded attentive generative network is designed to coarse-to-fine generate phrase patch by paying attention to the most relevant words in the caption. In addition, a phrase-wise DAMSM is proposed to better supervise the fine-grained phrase-patch consistency. On COCO dataset, our method outperforms the state-of-the-art methods on both Inception Score and FID while maintaining high visual quality. Extensive experiments demonstrate the unique features of our proposed method.