  The emergence of ChatGPT has once again sparked research in generative
artificial intelligence (GAI). While people have been amazed by the generated
results, they have also noticed the reasoning potential reflected in the
generated textual content. However, this current ability for causal reasoning
is primarily limited to the domain of language generation, such as in models
like GPT-3. In visual modality, there is currently no equivalent research.
Considering causal reasoning in visual content generation is significant. This
is because visual information contains infinite granularity. Particularly,
images can provide more intuitive and specific demonstrations for certain
reasoning tasks, especially when compared to coarse-grained text. Hence, we
propose a new image generation task called visual question answering with image
(VQAI) and establish a dataset of the same name based on the classic
\textit{Tom and Jerry} animated series. Additionally, we develop a new paradigm
for image generation to tackle the challenges of this task. Finally, we perform
extensive experiments and analyses, including visualizations of the generated
content and discussions on the potentials and limitations. The code and data
are publicly available under the license of CC BY-NC-SA 4.0 for academic and
non-commercial usage. The code and dataset are publicly available at:
https://github.com/IEIT-AGI/MIX-Shannon/blob/main/projects/VQAI/lgd_vqai.md.

Computer Vision and Pattern Recognition;Multimedia