NTIRE 2025视频质量评估挑战赛，评测用户、AI生成与对话头像三类视频质量。
NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results
- 分三赛道评测视频质量：用户生成、AI生成和对话头像。
- 共收396份开发阶段提交，136份测试阶段提交，多团队超越基线。
- 涵盖细粒度数据集，推动视频质量评估技术发展。
本文报告了将在CVPR 2025 NTIRE研讨会期间举办的NTIRE 2025 XGC质量评估挑战赛。该挑战聚焦视频与对话头像处理中的核心难题，分为三大赛道：用户生成视频、AI生成视频和对话头像。用户生成视频赛道使用FineVD-GC数据集，包含6,284条用户上传视频，共有125名注册参与者，开发阶段收到242份提交，测试阶段136份，最终5支队伍提交模型与说明文档。AI生成视频赛道基于Q-Eval-Video数据集，包含34,029条由11种主流文本到视频（T2V）模型生成的AI生成视频（AIGVs），共133名参与者，开发阶段提交396次，测试阶段226次，最终6支队伍完成提交。对话头像赛道采用THQA-NTIRE数据集，包含12,247个2D与3D对话头像，89名参与者，开发阶段225份提交，测试阶段118份，最终8支队伍完成提交。各赛道所有参赛团队提出的方案均优于基线，推动了相关领域的发展。
原文摘要 · Abstract (English)
This paper reports on the NTIRE 2025 XGC Quality Assessment Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2025. This challenge is to address a major challenge in the field of video and talking head processing. The challenge is divided into three tracks, including user generated video, AI generated video and talking head. The user-generated video track uses the FineVD-GC, which contains 6,284 user generated videos. The user-generated video track has a total of 125 registered participants. A total of 242 submissions are received in the development phase, and 136 submissions are received in the test phase. Finally, 5 participating teams submitted their models and fact sheets. The AI generated video track uses the Q-Eval-Video, which contains 34,029 AI-Generated Videos (AIGVs) generated by 11 popular Text-to-Video (T2V) models. A total of 133 participants have registered in this track. A total of 396 submissions are received in the development phase, and 226 submissions are received in the test phase. Finally, 6 participating teams submitted their models and fact sheets. The talking head track uses the THQA-NTIRE, which contains 12,247 2D and 3D talking heads. A total of 89 participants have registered in this track. A total of 225 submissions are received in the development phase, and 118 submissions are received in the test phase. Finally, 8 participating teams submitted their models and fact sheets. Each participating team in every track has proposed a method that outperforms the baseline, which has contributed to the development of fields in three tracks.
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