arXiv:2606.30026v1 Announce Type: cross Abstract: Audiovisual arts encompass diverse creative disciplines, including cinema, visual arts, stage performance, and game design, where artistic meaning arises from deliberate combinations of visual, auditory, and narrative elements (e.g., fear amplified through claustrophobic framing, or grief conveyed through silence and lingering close-ups). True artistic understanding extends beyond recognizing what is depicted to reasoning about why it is expressed through particular creative choices. Despite the strong progress of multimodal large language mode
The proliferation of sophisticated multimodal large language models (MLLMs) necessitates benchmarks that evaluate nuanced, intent-level understanding beyond basic recognition.
Measuring and advancing MLLMs' ability to understand artistic intent moves them closer to human-level comprehension, critical for creative industries and advanced AI applications.
The introduction of MuseBench provides a new standard for evaluating MLLMs, pushing research towards more sophisticated audiovisual reasoning capabilities.
- · AI researchers
- · MLLM developers
- · Creative industries using AI
- · MLLMs lacking advanced reasoning
- · Simplistic AI benchmarking methods
MuseBench will drive the development of MLLMs with deeper, more human-like understanding of complex artistic expressions.
Improved MLLMs could significantly enhance content creation, analysis, and personalized experiences across cinema, gaming, and visual arts.
These advancements might lead to fully autonomous AI agents capable of generating and interpreting sophisticated artistic works, blurring the lines of authorship.
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Read at arXiv cs.AI