Rapid advances in AI-generated video (AIGV) have increased the risks posed by deceptive video manipulation. Unlike fully synthetic videos, manipulated videos retain most source content and alter only localized regions, making forensic analysis particularly challenging. Existing video forgery research faces two limitati...
Heng-Rui Kang, Zhong-Hao Yan, Yuxuan Yang et al.· 0 citations
Despite its crucial role in video object removal (VOR), existing evaluation paradigms face two critical limitations: questionable references and a misalignment between tradi- tional metrics and human preference. To address these challenges, we introduce VOR- Bench, which advances VOR evaluation through three integrated...
Hao-Nan Huang, Tian-Rui Qiu, Xiang-Hao Zang et al.· 0 citations
MMBench-Live is presented, a continuously evolving multimodal benchmark built by a multi-agent-driven automated pipeline that preserves stable model rankings, maintains semantic alignment with the original benchmark, and exhibits weaker contamination-related memorization signals, suggesting a practical and scalable par...
Yuanzhi Liu, Shousheng Zhao, Bo Zhou et al.· 0 citations
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