Text2Traffic: 一种用于交通场景的文本到图像生成与编辑方法
Text2Traffic: A Text-to-Image Generation and Editing Method for Traffic Scenes
AI总结：
本文提出了一种统一的文本驱动框架，通过可控掩码机制和多视角数据增强，提升交通场景中文本到图像生成与编辑的保真度和对齐性。
摘要：
随着智能交通系统的快速发展，基于文本的图像生成和编辑技术在为交通监控和自动驾驶等应用提供丰富、可控的视觉场景数据方面展现出巨大潜力。然而，仍存在诸多挑战，包括生成交通元素的语义丰富性不足、摄像机视角有限、合成图像的视觉保真度低，以及文本描述与生成内容之间的对齐性差。为了解决这些问题，我们提出了一种统一的基于文本的框架，用于图像生成和编辑，利用可控的掩码机制无缝整合两项任务。此外，我们还结合车辆侧和道路侧的多视角数据，以增强交通场景的几何多样性。我们的训练策略采用两阶段范式：首先，我们使用大规模粗粒度的文本-图像数据进行概念学习；然后，我们使用细粒度的描述数据进行微调，以增强文本-图像对齐性和细节质量。此外，我们引入了掩码区域加权损失，该损失在训练过程中动态强调小但关键的区域，从而显著提高了小规模交通元素的生成保真度。大量实验表明，我们的方法在交通场景的文本驱动图像生成和编辑中取得了领先性能。
英文摘要：
With the rapid advancement of intelligent transportation systems, text-driven image generation and editing techniques have demonstrated significant potential in providing rich, controllable visual scene data for applications such as traffic monitoring and autonomous driving. However, several challenges remain, including insufficient semantic richness of generated traffic elements, limited camera viewpoints, low visual fidelity of synthesized images, and poor alignment between textual descriptions and generated content. To address these issues, we propose a unified text-driven framework for both image generation and editing, leveraging a controllable mask mechanism to seamlessly integrate the two tasks. Furthermore, we incorporate both vehicle-side and roadside multi-view data to enhance the geometric diversity of traffic scenes. Our training strategy follows a two-stage paradigm: first, we perform conceptual learning using large-scale coarse-grained text-image data; then, we fine-tune with fine-grained descriptive data to enhance text-image alignment and detail quality. Additionally, we introduce a mask-region-weighted loss that dynamically emphasizes small yet critical regions during training, thereby substantially enhancing the generation fidelity of small-scale traffic elements. Extensive experiments demonstrate that our method achieves leading performance in text-based image generation and editing within traffic scenes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
