Abstract
Environment designers in the entertainment industry create imaginative 2D and 3D scenes for games, films, and television, requiring both fine-grained control of specific details and consistent global coherence. Designers have increasingly integrated generative AI into their workflows, often relying on large language models (LLMs) to expand user prompts for text-to-image generation, then iteratively refining those prompts and applying inpainting. However, our formative study with 10 designers surfaced two key challenges: (1) the lengthy LLM-generated prompts make it difficult to understand and isolate the keywords that must be revised for specific visual elements; and (2) while inpainting supports localized edits, it can struggle with global consistency and correctness. Based on these insights, we present GenTune, an approach that enhances human--AI collaboration by clarifying how AI-generated prompts map to image content. Our GenTune system lets designers select any element in a generated image, trace it back to the corresponding prompt labels, and revise those labels to guide precise yet globally consistent image refinement. In a summative study with 20 designers, GenTune significantly improved prompt--image comprehension, refinement quality, and efficiency, and overall satisfaction (all $p < .01$) compared to current practice. A follow-up field study with two studios further demonstrated its effectiveness in real-world settings.
Abstract (translated)
在娱乐行业中，环境设计师负责为游戏、电影和电视节目创造富有想象力的二维和三维场景。这项工作既需要对特定细节进行精细控制，又要求整体一致性。越来越多的设计人员将生成式人工智能（AI）整合到他们的工作流程中，通常利用大型语言模型（LLMs）来扩展用户指令以生成图像，并通过迭代优化这些提示并应用局部重绘技术（inpainting）。然而，在与10位设计师进行初步研究后发现两个主要挑战：一是由LLM生成的长指令使得难以理解及分离出必须修改特定视觉元素的关键字；二是虽然局部重绘支持对特定区域的编辑，但在保持全局一致性和准确性方面存在困难。 基于这些见解，我们提出了GenTune这一方法，旨在通过明确AI生成提示与图像内容之间的关联来增强人机协作。GenTune系统允许设计师选择生成图像中的任何元素，并将其追溯到相应的提示标签，随后修改这些标签以引导精确而全局一致的图像优化过程。在后续对20位设计师进行的总结性研究中，与现行做法相比，使用GenTune显著提高了指令-图像的理解程度、优化质量以及效率和总体满意度（所有p值均小于.01）。此外，在两个工作室进行的跟进实地研究进一步证明了该系统在实际环境中的有效性。
URL
https://arxiv.org/abs/2508.15227
