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MuseBench is a comprehensive benchmark introduced to evaluate multimodal large language models on nuanced, intent-level understanding of audiovisual arts, revealing that even the best model achieves only 48.29% accuracy compared to 87.18% for human experts.
CogOmniControl is a reasoning-driven framework for controllable video generation that uses a specialized vision-language model (CogVLM) trained on anime production data to infer creative intent from sparse conditions, then guides a diffusion-based generator via reinforcement learning, achieving state-of-the-art results on new benchmarks.