TAKE 85: Testing Audiovisual filmmaKer's intEnt across 85 Hours of Film
Films communicate through deliberate creative choices, including lighting, color, composition, editing, dialogue, music, and sound. Humans naturally interpret these signals as directorial intent, yet current multimodal large language models (MLLMs) are evaluated almost exclusively on understanding what happens rather than why it is presented that way. We introduce TAKE 85, the first benchmark for directorial-intent understanding, comprising 398 short films (85 hours) with expert-verified question-answer pairs spanning global and fine-grained visual and audio intent. Through controlled modality ablations, TAKE 85 enables systematic evaluation of multimodal reasoning. Experiments on state-of-the-art MLLMs reveal a substantial gap between perceptual recognition and intentional understanding: while models accurately describe events and narratives, they consistently fail to infer the communicative role of filmmaking decisions. Our results establish directorial intent as a previously overlooked dimension of multimodal understanding: even the strongest model reaches only 58 out of 100, and our ablations show that no input modality is sufficient on its own. All code, Q&As, and models are publicly available from https://github.com/KaiShinozakiConefrey/Take-85
Code (0)
등록된 구현이 없습니다.
Tasks
Multimodal ReasoningSimilar Papers 제목 키워드 기반
On the Role of Noise in AudioVisual Integration: Evidence from Artificial Neural Networks that Exhibit the McGurk Effect
Humans are able to fuse information from both auditory and visual modalities to help with understanding speech. This is frequently demonstrated through an phenomenon known as the McGurk Effect, during which a listener is…
Matching visual induction effects on screens of different size
In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a sce…
Development and Evaluation of Video Recordings for the OLSA Matrix Sentence Test
One of the established multi-lingual methods for testing speech intelligibility is the matrix sentence test (MST). Most versions of this test are designed with audio-only stimuli. Nevertheless, visual cues play an import…
SentenceModality Dropout for Improved Performance-driven Talking Faces
We describe our novel deep learning approach for driving animated faces using both acoustic and visual information. In particular, speech-related facial movements are generated using audiovisual information, and non-spee…
OWL (Observe, Watch, Listen): Audiovisual Temporal Context for Localizing Actions in Egocentric Videos
Egocentric videos capture sequences of human activities from a first-person perspective and can provide rich multimodal signals. However, most current localization methods use third-person videos and only incorporate vis…
Action LocalizationTemporal Action LocalizationTemporal Localization