Owl-1: Omni World Model for Consistent Long Video Generation (2412.09600v1)

Abstract: Video generation models (VGMs) have received extensive attention recently and serve as promising candidates for general-purpose large vision models. While they can only generate short videos each time, existing methods achieve long video generation by iteratively calling the VGMs, using the last-frame output as the condition for the next-round generation. However, the last frame only contains short-term fine-grained information about the scene, resulting in inconsistency in the long horizon. To address this, we propose an Omni World modeL (Owl-1) to produce long-term coherent and comprehensive conditions for consistent long video generation. As videos are observations of the underlying evolving world, we propose to model the long-term developments in a latent space and use VGMs to film them into videos. Specifically, we represent the world with a latent state variable which can be decoded into explicit video observations. These observations serve as a basis for anticipating temporal dynamics which in turn update the state variable. The interaction between evolving dynamics and persistent state enhances the diversity and consistency of the long videos. Extensive experiments show that Owl-1 achieves comparable performance with SOTA methods on VBench-I2V and VBench-Long, validating its ability to generate high-quality video observations. Code: https://github.com/huang-yh/Owl.

• The paper introduces a novel state-observation-dynamics model integrating latent states, explicit observations, and dynamic elements to maintain long-term video coherence.
• It employs an autoregressive approach with pretrained large multimodal models to predict future dynamics, ensuring subject and background consistency.
• Evaluations on benchmarks like VBench-I2V and VBench-Long demonstrate state-of-the-art performance while revealing areas for improved dynamic diversity.

An In-depth Analysis of Owl-1: Omni World Model for Consistent Long Video Generation

The paper "Owl-1: Omni World Model for Consistent Long Video Generation" introduces an innovative approach to video generation, specifically focusing on achieving consistency across long video sequences—a known challenge in the domain of Video Generation Models (VGMs). While current VGMs are efficient at generating short clips, extending their capabilities to longer video sequences has presented challenges, particularly in maintaining temporal consistency. Owl-1 addresses this by leveraging a concept termed the "Omni World Model," which integrates latent state variables, explicit observations, and dynamic elements to model the evolving world from which video sequences are derived.

Core Contributions and Methodology

The primary contribution of Owl-1 lies in its ability to model long-term co-evolution of video content using a state-observation-dynamics triplet. Central to this approach is the latent state variable, which encodes both present and historical information regarding the underlying world context. This latent state is critical in maintaining long-term coherence while generating videos, moving beyond short-term, clip-based prompts commonly used in existing methods.

Key methodological elements include:

• State-Observation-Dynamics Model: Owl-1 employs an autoregressive model to simulate world evolution. Latent state variables enable comprehensive condition encoding for video generation, from which explicit video observations can be decoded using video diffusion models.
• Anticipation of Future Dynamics: The model predicts the future dynamics of the world by deriving them from the current state and the previously rendered observations, enabling the generation of varied and coherent content over extended durations.
• Pretrained Large Multimodal Model (LMM): To exploit common knowledge and ensure temporal coherence, Owl-1 incorporates pretrained LMMs to facilitate comprehensive modeling across visual and text modalities.

Results and Evaluations

Owl-1 demonstrates effectiveness across various benchmark tests, including VBench-I2V and VBench-Long, which are designed to assess short and long video generation capabilities, respectively. Comparatively, Owl-1 attains performance akin to state-of-the-art methods in critical dimensions such as subject and background consistency, motion smoothness, and minimal temporal flickering—a testament to its robustness in generating temporally consistent video content.

One aspect where Owl-1 shows room for improvement is in the dynamic diversity of generated content. This shortfall may be attributable to the scarcity of training datasets containing sufficient high-motion-level video data, which could enrich the dynamic range achievable by the model.

Implications and Future Work

The implications of developing such a world model for long video generation are extensive, both theoretically and practically:

• Theoretical Advances: Owl-1's framework may significantly contribute to the development of general-purpose video generation models adept at learning intricate spatiotemporal relationships inherent in video data.
• Practical Applications: The capability to generate consistent long videos has broad applications, ranging from entertainment industries to automated surveillance systems and interactive simulations.

Future work should concentrate on addressing the limitations concerning dynamic diversity. This can be pursued by incorporating richer datasets with varied dynamic scenes and exploring scalable training regimes that effectively utilize larger and more diverse data pools. Additionally, further refinement of transition control between scenes can enhance the capability of Owl-1 in maintaining coherent narrative flows in complex video sequences.

In conclusion, Owl-1 presents a methodical advancement in the field of video generation by modeling the underlying dynamics of an evolving world, enabling consistent long video generation. However, further exploration into overcoming existing limitations could pave the way for more refined and adaptable video generation systems.