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MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators (2404.05014v1)

Published 7 Apr 2024 in cs.CV

Abstract: Recent advances in Text-to-Video generation (T2V) have achieved remarkable success in synthesizing high-quality general videos from textual descriptions. A largely overlooked problem in T2V is that existing models have not adequately encoded physical knowledge of the real world, thus generated videos tend to have limited motion and poor variations. In this paper, we propose \textbf{MagicTime}, a metamorphic time-lapse video generation model, which learns real-world physics knowledge from time-lapse videos and implements metamorphic generation. First, we design a MagicAdapter scheme to decouple spatial and temporal training, encode more physical knowledge from metamorphic videos, and transform pre-trained T2V models to generate metamorphic videos. Second, we introduce a Dynamic Frames Extraction strategy to adapt to metamorphic time-lapse videos, which have a wider variation range and cover dramatic object metamorphic processes, thus embodying more physical knowledge than general videos. Finally, we introduce a Magic Text-Encoder to improve the understanding of metamorphic video prompts. Furthermore, we create a time-lapse video-text dataset called \textbf{ChronoMagic}, specifically curated to unlock the metamorphic video generation ability. Extensive experiments demonstrate the superiority and effectiveness of MagicTime for generating high-quality and dynamic metamorphic videos, suggesting time-lapse video generation is a promising path toward building metamorphic simulators of the physical world.

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MagicTime: Unveiling the Method behind Metamorphic Time-Lapse Video Generation

Introduction to Metamorphic Video Generation

The domain of Text-to-Video (T2V) generation has recently made significant strides, notably with the advent of diffusion models. Yet, an intriguing area that eludes most current T2V models is the generation of metamorphic videos - a type that encodes extensive physical world knowledge through the depiction of object transformations like melting, blooming, or construction. Unlike general videos, which primarily capture camera motion or static scene changes, metamorphic videos cover the complete transformation process of subjects, presenting a rich tapestry of physical changes. Addressing this gap, the MagicTime framework emerges, innovatively leveraging time-lapse videos to infer real-world physics and metamorphosis, encapsulating these phenomena in high-quality metamorphic videos.

Core Contributions of MagicTime

MagicTime introduces several key methodologies to empower metamorphic video generation:

  • MagicAdapter Scheme: Strategically decouples spatial and temporal training, incorporating a MagicAdapter to infuse physical knowledge from metamorphic videos into pre-trained T2V models. This enables the generation of videos that not only maintain general content quality but also accurately depict complex transformations.
  • Dynamic Frames Extraction: Tailors the model to accommodate the unique characteristics of time-lapse training videos, ensuring emphasis on metamorphic features over standard video elements. This approach significantly enriches the model's comprehension and portrayal of physical processes.
  • Meta Text-Encoder: Enhances text prompt understanding, particularly targeting metamorphic video generation. This refinement allows for more precise adherence to the descriptive nuances present in prompts for metamorphic content.
  • ChronoMagic Dataset Construction: A meticulously curated dataset specifically designed for metamorphic video generation, consisting of 2,265 time-lapse video-text pairs. This dataset serves as a foundational tool to facilitate model training and benchmarking within the metamorphic video generation field.

Empirical Validation and Dataset Benchmarking

Extensive experiments underscore MagicTime's superior performance in generating dynamic, high-quality metamorphic videos. Leveraging the ChronoMagic dataset, MagicTime demonstrates remarkable proficiency in embodying real-world physical transformations within generated content, setting new benchmarks across established metrics such as FID, FVD, and CLIPSIM.

Theoretical and Practical Implications

From a theoretical perspective, MagicTime elucidates the importance of encoding physical knowledge within T2V models, representing a novel approach towards comprehensively understanding real-world dynamics. Practically, MagicTime opens up diverse applications ranging from educational content creation, simulation of environmental changes, to the enhancement of creative media productions. Moreover, by introducing the ChronoMagic dataset, MagicTime provides a valuable resource for advancing research in metamorphic video generation.

Future Developments in Generative AI and Metamorphic Simulators

Looking forward, the progression of metamorphic video generation heralds transformative potentials in AI's ability to simulate and predict complex physical and environmental changes. The evolution of frameworks like MagicTime could significantly contribute to fields such as climate modeling, architectural visualization, and beyond. Moreover, integrating advanced natural language processing techniques could further refine the model's responsiveness to complex descriptive prompts, enhancing the fidelity and scope of generated content.

In conclusion, MagicTime represents a pivotal step towards bridging the gap between generative models and the nuanced depiction of physical transformations. By doing so, it not only advances the field of T2V generation but also broadens the horizons for AI applications in simulating the physical world.

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Authors (9)
  1. Shenghai Yuan (92 papers)
  2. Jinfa Huang (25 papers)
  3. Yujun Shi (23 papers)
  4. Yongqi Xu (11 papers)
  5. Ruijie Zhu (22 papers)
  6. Bin Lin (33 papers)
  7. Xinhua Cheng (21 papers)
  8. Li Yuan (141 papers)
  9. Jiebo Luo (355 papers)
Citations (17)
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