Paper Detail
Shiwen Wang, Jian Yang, Xu Wang, Xincan Wang, Weiming Dong
Positive and negative space is a fundamental principle in visual composition, supporting visually coherent forms and layered semantic relationships. Generating such compositions is challenging because it requires coordinated control over two semantic concepts that share a common boundary. Although recent text-to-image models and multimodal large language models (MLLMs) have achieved strong performance in image generation and visual understanding, positive-negative space generation remains difficult, particularly under direct single-pass prompting. In this work, we present the \textbf{F}orm \textbf{a}nd \textbf{V}oid \textbf{A}gent (\textbf{FaV-A}), a multimodal agent designed for staged positive-negative space generation. FaV-A follows a progressive workflow: it first generates a base object, then analyzes its shape and spatial structure to identify candidate negative-space semantics, and finally produces compositional instructions for the final image generation stage. Experimental results and ablation analyses suggest that FaV-A provides a more effective framework than direct zero-shot MLLM baselines for producing visually coherent and semantically aligned positive-negative space compositions.
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@article{wang2026form,
title = {Form and Void: Entangled Composition through an Autonomous AI Agent},
author = {Shiwen Wang and Jian Yang and Xu Wang and Xincan Wang and Weiming Dong},
year = {2026},
abstract = {Positive and negative space is a fundamental principle in visual composition, supporting visually coherent forms and layered semantic relationships. Generating such compositions is challenging because it requires coordinated control over two semantic concepts that share a common boundary. Although recent text-to-image models and multimodal large language models (MLLMs) have achieved strong performance in image generation and visual understanding, positive-negative space generation remains diffic},
url = {https://arxiv.org/abs/2610.02045},
keywords = {cs.CV, cs.MA},
eprint = {2610.02045},
archiveprefix = {arXiv},
}
{}