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The article notes that while AI excels at generating convincing single frames, true filmmaking success depends on integrating camera, characters, and environment across entire sequences.
This paper investigates whether dependency parsing of non-human primate vocalizations or gestures can be evaluated without a gold standard. Using network science, the authors show that the proportion of correct edges retrieved by a parser is necessarily high due to the fast decay of sequence length distributions in non-human primates, making evaluation feasible, unlike for human language.