Creating Hilarious Comic Dialogs with AI

Table of Contents:

1. Introduction
2. The Problem of Comic Dialogue Generation
3. The Comic Strip and its Components 3.1 Visual Inputs 3.2 Text Inputs 3.3 Personal Facts
4. Existing Work in Dialogue Generation and Multimodal Datasets
5. The Comset Dataset 5.1 Data Collection and Cleaning 5.2 Data Preprocessing and Parsing 5.3 Panel Segmentation and Text Detection 5.4 Multimodal Alignment 5.5 Persona Fact Generation 5.6 Dataset Statistics
6. The MP Dialog Model 6.1 Model Architecture 6.2 Baseline Models 6.3 Results and Evaluation
7. Qualitative Analysis 7.1 Sample Comic Strip from Garfield 7.2 Vocabulary Distribution Comparison 7.3 Human Evaluation of Model Responses
8. Conclusion and Future Work

Article:

Comic Dialogue Generation: A Multi-Modal Approach

Introduction

In the world of comics, dialogue plays a crucial role in shaping the narrative and character development. The ability to generate compelling and contextually Relevant dialogues for comic strips is a challenging task. This article explores the problem of comic dialogue generation and presents a new approach that leverages multi-modal inputs, including visual scenes, text inputs, and personal facts. We introduce the Comset dataset, a comprehensive collection of comic strips with associated transcripts and images. Additionally, we propose the MP Dialog model, which incorporates personal information and visual cues to generate coherent and persona-consistent comic dialogues.

The Problem of Comic Dialogue Generation

Comic dialogue generation involves predicting the next utterance for a given comic strip, taking into account the conversation history, visual inputs, and personal facts associated with the characters. The goal is to generate dialogues that are both contextually appropriate and consistent with each character's persona. This task presents several challenges, such as incorporating multi-modality in dialogue models, handling multi-party dialogues with diverse personas, and capturing humor in generative models.

The Comic Strip and its Components

A comic strip consists of visual scenes and text inputs, accompanied by personal facts about the characters. The visual inputs provide Context and visual cues for generating the next dialogue. The text inputs include the conversation history and the utterances spoken so far. The personal facts are specific details about each character, such as their personality traits, affiliations, or roles within the comic strip. By considering these components, we can Create a holistic model for comic dialogue generation.

Existing Work in Dialogue Generation and Multimodal Datasets

Prior research has explored dialogue generation using language models like Dialog GPT and Edge. These models focus on generating coherent responses but do not incorporate visual or personal information. Multimodal datasets, such as Comics, provide image-text pairs for dialogue generation. However, existing models have not fully utilized these datasets for dialogue generation.

The Comset Dataset

To address the limitations of existing datasets, we introduce the Comset dataset, a comprehensive collection of 54,000 comic strips from various sources. The dataset includes transcripts, images, and personal facts associated with the characters. We curated the dataset by removing duplicate strips and performing preprocessing steps like parsing, panel segmentation, text detection, multimodal alignment, and persona fact generation. The Comset dataset allows researchers to develop and evaluate models for comic dialogue generation effectively.

The MP Dialog Model

The MP Dialog model is a multi-modal dialogue generation model designed specifically for comic strips. It incorporates personal information, text inputs, and visual scenes to generate contextually coherent and persona-consistent dialogues. The model consists of a text embedding module, a visual embedding module, and a multi-modal embedding module that combines the textual and visual information. The MP Dialog model outperforms baseline models in terms of perplexity, dialogue consistency, and engagement.

Qualitative Analysis

We conducted a qualitative analysis to evaluate the effectiveness of the MP Dialog model. We compared the responses generated by different models on a sample comic strip from Garfield. The results Show that the MP Dialog model produces more contextually appropriate and engaging responses compared to the baseline models.

We also analyzed the character-level vocabulary distributions of the generated responses and compared them to the training set. The KL divergence between the distributions indicates that the MP Dialog model's generated responses closely Resemble the vocabulary used in the training set, ensuring consistent and persona-aligned dialogues.

Furthermore, a human evaluation was conducted to assess the fluency, engagingness, dialogue consistency, and persona detection of the model responses. The MP Dialog model performed favorably in all metrics, demonstrating its ability to generate high-quality and persona-consistent comic dialogues.

Conclusion and Future Work

In this article, we presented a multi-modal approach to comic dialogue generation, incorporating personal information and visual cues. The Comset dataset provides a valuable resource for researchers in this field, enabling the development of more advanced dialogue generation models. The MP Dialog model showcased promising results, but there is still scope for improvement, such as refining response length and capturing humor in generated dialogues. Future work could also explore joint generation of image panels and utterances for a more comprehensive multimodal dialogue generation system.

In summary, our research endeavors to enhance the quality and coherence of comic dialogue generation, contributing to the development of advanced multimodal dialogue systems in the future.

Highlights:

• Comic dialogue generation is a challenging task that requires contextually appropriate and persona-consistent dialogues.
• The Comset dataset comprises 54,000 comic strips with transcripts, images, and personal facts, facilitating effective research on comic dialogue generation.
• The MP Dialog model incorporates personal information, text inputs, and visual scenes to generate coherent and engaging comic dialogues.
• Qualitative analysis and human evaluation demonstrate the effectiveness of the MP Dialog model in producing high-quality and persona-consistent responses.
• Future work involves refining response length, capturing humor in generated dialogues, and exploring joint generation of image panels and utterances.

FAQ:

Q: How was the Comset dataset created? A: The Comset dataset was created by gathering data from 13 comics and removing duplicate strips. The dataset underwent extensive cleaning, preprocessing, parsing, panel segmentation, text detection, multimodal alignment, and persona fact generation processes to ensure its quality and usability.

Q: What are the advantages of using the MP Dialog model for comic dialogue generation? A: The MP Dialog model outperforms baseline models by incorporating personal information, text inputs, and visual cues. It generates contextually coherent and persona-consistent dialogues, resulting in more engaging and authentic comic strip conversations.

Q: Can the MP Dialog model capture humor in generated dialogues? A: While the MP Dialog model focuses on contextually coherent dialogue generation, capturing humor is a complex task. Future work aims to explore ways to infuse humor into generated dialogues, providing a more immersive and entertaining comic reading experience.

Q: How does the MP Dialog model handle multi-party dialogues? A: The MP Dialog model considers the personal facts associated with each character in multi-party dialogues. By incorporating persona information, the model generates dialogues that are consistent with each character's traits and affiliations, enhancing the overall coherence and authenticity of the conversation.