In this work, we investigate the task of text-to-image (T2I) synthesis under the abstract-to-intricate setting, i.e., generating intricate visual content from simple

For object recognition convolutional neural networks, the shape bias leads to greater robustness against style and pattern change distraction.

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis.

To address these problems, in this paper we explore a simple yet effective decompositional approach towards both evaluation and improvement of text-to-image alignment.

In this paper, we propose the M asked S pace-T ime H ash encoding (MSTH), a novel method for efficiently reconstructing dynamic 3D scenes from multi-view or monoc-ular videos.

These estimates are subsequently utilized to aggregate information from reference frames into the target frame. Subsequently, hierarchical neural point clouds are constructed based on the aggregated information. This hierarchical point cloud set is then employed to synthesize views of the target frame.

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts.