MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions

🌈 Abstract

The article discusses the MiraData dataset, a new video dataset for video generation, and the potential societal impacts of advancements in video generation technology.

🙋 Q&A

[01] Scope of Sources, Annotation Quality, Video Length, Evaluation Metrics, Model Dependency, Ethical Considerations, and Generalizability

1. What are the limitations and potential biases of the MiraData dataset?

• The scope of sources used to create the dataset may have inherent biases, leading to underrepresentation of certain video genres or content types.
• The automatic annotation process used to generate captions and descriptions may introduce errors or inconsistencies, despite extensive manual verification.
• The average video duration in the dataset may not fully capture the complexity and nuances of real-world video content, especially for applications requiring extended temporal coherence.
• The novel metrics introduced to assess temporal consistency and motion strength are still subject to ongoing validation.
• The experiments conducted to demonstrate the utility of MiraData are based on a specific video generation model, DiT-based MiraDiT, and the observed advantages may not directly translate to other models or architectures.
• The manual selection and curation process for MiraData might inadvertently include content with ethical or copyright concerns, requiring continuous monitoring and ethical review.
• The specific enhancements made in MiraData, such as increased motion intensity and detailed captions, may be particularly suited for certain applications but not for others, and researchers must consider the domain-specific applicability of the dataset.

[02] Misinformation and Deepfakes, Privacy Concerns, Content Moderation Challenges, Intellectual Property Violations, Bias and Representation, Economic Displacement, Psychological Impact, and Ethical Use of AI

1. What are the potential negative societal impacts of advancements in video generation technology?

• Enhanced video generation capabilities can be misused to create highly realistic fake videos, potentially spreading misinformation or propaganda, which can undermine trust in digital media and have serious implications for public discourse, elections, and personal reputations.
• The creation and dissemination of realistic synthetic videos raise significant privacy issues, as individuals may be depicted in situations they were never a part of, leading to potential defamation or unauthorized use of likenesses.
• The ability to generate high-quality, realistic videos poses challenges for content moderation, as platforms may struggle to effectively identify and mitigate the distribution of violent, explicit, or otherwise harmful generated videos.
• The use of curated video sources and the generation of new content can lead to intellectual property concerns, with a risk of infringing on copyrights, trademarks, or other intellectual property rights, leading to legal disputes and ethical challenges.
• The dataset curation process, despite efforts to be diverse, might still reflect inherent biases, which can propagate into generated content, reinforcing stereotypes or marginalizing certain groups. Ensuring fair and unbiased representation in generated videos remains a complex challenge.
• Advances in video generation technology threaten jobs in industries reliant on human creativity and labor, such as film production, video editing, and content creation, potentially leading to economic displacement and requiring significant workforce retraining initiatives.
• The proliferation of highly realistic synthetic videos can have psychological impacts on viewers, leading to confusion, anxiety, or distress when distinguishing between real and fake content becomes increasingly difficult.
• The powerful capabilities of AI-driven video generation necessitate ongoing ethical considerations, and ensuring that these tools are used responsibly and in ways that benefit society, rather than harm it, requires robust guidelines, regulations, and ethical frameworks.