Generating accurate character descriptions and dialogues in comics presents several challenges and limitations:
Ambiguity in Visual Cues: Comics often rely on visual cues, such as facial expressions and body language, to convey emotions and intentions. In complex interactions where these cues are subtle or ambiguous, the model may struggle to accurately interpret the context, leading to mischaracterization or misrepresentation of dialogues.
Character Clustering Limitations: The effectiveness of character clustering algorithms can be hindered by overlapping character appearances, especially in dynamic scenes where characters change outfits or positions frequently. This can result in incorrect associations between dialogues and characters, complicating the generation of accurate descriptions.
Contextual Dependencies: Dialogues in comics are often context-dependent, with characters referencing past interactions or events. The model may face difficulties in maintaining continuity and coherence in dialogues, particularly when the context spans multiple panels or pages.
Cultural Nuances: Different comic genres may employ unique cultural references, humor, or idiomatic expressions that are challenging for the model to interpret accurately. This can lead to a lack of authenticity in character dialogues, especially in non-English comics.
Complex Narrative Structures: Comics with non-linear storytelling or multiple subplots can complicate the generation of coherent character descriptions and dialogues. The model must effectively track various narrative threads and their interconnections, which can be a significant challenge.