MoHallBench: A Benchmark for Motion Hallucination in Video Large Language Models
Video Large Language Models (VideoLLMs) have shown strong progress in video understanding, yet they still suffer from hallucinations that are inconsistent with visual evidence. Existing benchmarks mainly focus on object hallucination or coarse action perception, leaving a key video-specific problem underexplored: motion hallucination, in which models infer human motions that are absent from the video. We present MoHallBench, a benchmark for diagnosing motion hallucination in VideoLLMs. MoHallBench systematically evaluates three major sources of hallucination: co-occurrence priors, sequential i
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- Linked via arxiv authorJiale Li →
MoHallBench: A Benchmark for Motion Hallucination in Video Large Language Models
- Linked via arxiv authorSihan Chen →
MoHallBench: A Benchmark for Motion Hallucination in Video Large Language Models
- Linked via arxiv authorMengyuan Liu →
MoHallBench: A Benchmark for Motion Hallucination in Video Large Language Models