首个机器人生成视频质量评估数据库，揭示现有模型在复杂场景下的不足。
RGC-VQA: An Exploration Database for Robotic-Generated Video Quality Assessment
- 构建包含2100个视频的机器人生成内容数据库，覆盖三类机器人和多平台数据。
- 主观实验发现人类对机器人视角视频的感知与传统视频差异显著。
- 11个主流VQA模型在该数据集上表现不佳，凸显专用模型的必要性。
随着带摄像头的机器人平台日益融入日常生活，机器人生成视频开始出现在流媒体平台上，预示着人机共存的未来。本文首次提出‘机器人生成内容’（RGC）概念，指代从机器人第一人称视角生成的视频。在人机交互场景中，RGC视频的感知质量至关重要，其特有的失真模式和视觉需求与专业制作内容（PGC）和用户生成内容（UGC）显著不同。然而，针对RGC视频的质量评估研究仍属空白。为填补这一空白并支持更广泛的机器人应用，我们建立了首个机器人生成内容数据库（RGCD），包含总计2100个视频，来自三类机器人，并源自多个平台。随后开展了主观视觉质量评估实验，以分析人类对机器人生成视频的感知。最后，通过基准实验评估了11个前沿VQA模型在该数据库上的表现。结果表明，现有模型在处理复杂机器人生成内容时存在显著局限，凸显开发RGC专用VQA模型的迫切需求。本数据库已公开于：https://github.com/IntMeGroup/RGC-VQA。
原文摘要 · Abstract (English)
As camera-equipped robotic platforms become increasingly integrated into daily life, robotic-generated videos have begun to appear on streaming media platforms, enabling us to envision a future where humans and robots coexist. We innovatively propose the concept of Robotic-Generated Content (RGC) to term these videos generated from egocentric perspective of robots. The perceptual quality of RGC videos is critical in human-robot interaction scenarios, and RGC videos exhibit unique distortions and visual requirements that differ markedly from those of professionally-generated content (PGC) videos and user-generated content (UGC) videos. However, dedicated research on quality assessment of RGC videos is still lacking. To address this gap and to support broader robotic applications, we establish the first Robotic-Generated Content Database (RGCD), which contains a total of 2,100 videos drawn from three robot categories and sourced from diverse platforms. A subjective VQA experiment is conducted subsequently to assess human visual perception of robotic-generated videos. Finally, we conduct a benchmark experiment to evaluate the performance of 11 state-of-the-art VQA models on our database. Experimental results reveal significant limitations in existing VQA models when applied to complex, robotic-generated content, highlighting a critical need for RGC-specific VQA models. Our RGCD is publicly available at: https://github.com/IntMeGroup/RGC-VQA.
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