- The paper introduces Any2Caption, a framework that uses multimodal large language models to interpret diverse conditions (text, image, motion, etc.) into detailed captions for controllable video generation.
- The authors introduce the Any2CapIns dataset (337,000 instances) specifically designed for training condition-to-caption interpretation for video generation models.
- Experimental results demonstrate that using Any2Caption's structured captions significantly improves video control, quality, and intent reasoning compared to baselines.
Overview of the Any2Caption Framework for Controllable Video Generation
The paper "Any2Caption: Interpreting Any Condition to Caption for Controllable Video Generation" introduces a promising framework for enhancing video generation models through improved interpretation of user inputs. The authors present a novel approach, termed Any2Caption, which decouples condition interpretation from video synthesis, thereby enabling more precise control over generated video content from various multimodal inputs.
Core Contributions
The key contributions of this work lie in developing a method to address the limitations posed by existing video generation techniques in accurately interpreting user intentions. The authors argue that current models struggle to generate high-quality, controllable videos due to their inability to effectively translate diverse input conditions into actionable syntheses. To mitigate this, Any2Caption leverages modern multimodal LLMs (MLLMs) to convert varied inputs—spanning text, images, videos, motion, and camera poses—into dense, structured captions. These captions provide detailed guidance to the video generation backbone, enhancing controllability and quality without the need for extensive retraining.
Any2CapIns Dataset
To support the Any2Caption framework, the authors introduce Any2CapIns, a large-scale dataset consisting of 337,000 instances and 407,000 condition annotations. This dataset facilitates condition-to-caption instruction tuning by transforming concise user prompts and visual conditions into detailed captions. A significant effort is made in manual labeling and automated annotation, followed by rigorous verification, to ensure dataset quality. The dataset includes diverse conditions such as depth maps, identity references, human poses, and camera movements, which serve as a training ground for teaching the model to interpret user-intent under varying contexts.
Experimental Results
The results of comprehensive evaluations are highlighted, showcasing significant improvements in control and video quality across different types of video generation models when using Any2Caption. Comparisons against baseline models show that structured captions generated by Any2Caption yield noticeable enhancements in video outputs, demonstrating their efficacy as control signals for video synthesis. The method achieves high scores in structural integrity, lexical matching, semantic matching, and intent reasoning, proving its robust interpretation capabilities.
Future Implications
This research opens several pathways for future exploration in AI. The ability to convert complex conditions to detailed captions can be employed not only in filmmaking and media but also in interactive content creation platforms and virtual reality applications. The integration of MLLMs into video generation fosters improved multimodal reasoning, and as these models advance, the precision and diversity of generated content are expected to expand further. Potential developments could include refinement of alignment strategies and expansion of condition diversity to address edge scenarios.
Conclusion
Any2Caption represents a significant step forward in addressing the bottlenecks of multimodal condition interpretation in video generation. By decoupling condition interpretation from synthesis, it manages to leverage existing video generation architectures more efficiently, without additional tuning costs. The introduction of the comprehensive Any2CapIns dataset further facilitates training, setting a benchmark for future works in video generation. This work paves the way for enhancing the expressiveness and accuracy of generated videos, providing a robust foundation for advanced AI-driven content creation.