Vid-Freeze immunizes images by adding perturbations that target attention dynamics in I2V models to enforce temporal freezing and suppress motion synthesis.
VBench: Comprehensive benchmark suite for video generative models
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HuM-Eval evaluates human motion videos with a coarse-to-fine approach using VLM global checks plus 2D pose and 3D motion analysis, reaching 58.2% average correlation with human judgments and introducing a 1000-prompt benchmark.
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Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing
Vid-Freeze immunizes images by adding perturbations that target attention dynamics in I2V models to enforce temporal freezing and suppress motion synthesis.
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HuM-Eval: A Coarse-to-Fine Framework for Human-Centric Video Evaluation
HuM-Eval evaluates human motion videos with a coarse-to-fine approach using VLM global checks plus 2D pose and 3D motion analysis, reaching 58.2% average correlation with human judgments and introducing a 1000-prompt benchmark.