Flexible Diffusion Modeling of Long Videos (2205.11495v3)

Abstract: We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments. We introduce a generative model that can at test-time sample any arbitrary subset of video frames conditioned on any other subset and present an architecture adapted for this purpose. Doing so allows us to efficiently compare and optimize a variety of schedules for the order in which frames in a long video are sampled and use selective sparse and long-range conditioning on previously sampled frames. We demonstrate improved video modeling over prior work on a number of datasets and sample temporally coherent videos over 25 minutes in length. We additionally release a new video modeling dataset and semantically meaningful metrics based on videos generated in the CARLA autonomous driving simulator.

• The paper introduces a novel DDPM-based video generative model that integrates temporal attention and positional encoding to capture long-range dependencies.
• It employs a meta-learning objective to adapt to diverse video tasks and flexibly sample arbitrary frame sequences.
• Evaluated on benchmarks including the CARLA Town01 dataset, the approach generates up to 15,000-frame videos with enhanced temporal coherence.

Flexible Diffusion Modeling of Long Videos

In this paper, the authors introduce a novel approach to generative video modeling utilizing denoising diffusion probabilistic models (DDPM). Their framework specifically addresses the generation of long-duration, photo-realistic videos, which constitutes a substantial challenge given the scalability limitations of current hardware capabilities. The paper emphasizes flexible sampling of video frames and introduces a model, termed the Flexible Diffusion Model (FDM), which can sample or condition on an arbitrary subset of video frames, enabling robust exploration of various sampling schemes.

The authors highlight several contributions:

1. DDPM-Based Video Generative Model: This work presents one of the first instances of a DDPM-based architecture for video generation. It builds on existing image generation models by integrating a temporal attention mechanism and a relative position encoding network, allowing for handling temporal dependencies in video data.
2. Meta-Learning Objective: The framework employs a "meta-learning" training objective, allowing the model to adapt to various video generation tasks, whether it involves different frames being conditioned or different durations being generated.
3. Evaluation and Optimization of Sampling Schemes: The model facilitates the exploration and optimization of resource-constrained video generation schemes, yielding improvements over prior methodologies across multiple datasets, as measured by standard video quality metrics such as Fréchet Video Distance (FVD).
4. CARLA Town01 Dataset: The authors release a new dataset generated via the CARLA autonomous driving simulator, providing a benchmark that includes semantically meaningful performance metrics for video modeling. This dataset potentially enables the community to evaluate generative models on real-world driving scenarios.

The results demonstrated by the authors include the generation of highly coherent and temporally extended video sequences of up to 15,000 frames (approximately 25 minutes in duration) without notable degradation in sample quality. This exceeds the capabilities of previously established models limited to shorter video sequences. The experiments span diverse datasets, including synthetic environments like GQN-Mazes, procedurally generated gaming environments like MineRL, and the aforementioned CARLA autonomous driving dataset.

Theoretical and Practical Implications

The introduction of an effective DDPM-based video generation framework has important theoretical implications. By shifting towards diffusion probabilistic models, the authors highlight the advantages of this modeling paradigm in capturing long-range temporal dependencies while respecting the memory and processing constraints of available hardware. In a broader scope, this development paves the path for deeper integration of generative modeling technique advancements in temporal sequence prediction tasks.

Practically, the generative models architected through FDM and evaluated on the CARLA Town01 dataset offer substantial utility in autonomous driving. By training models on realistic driving scenarios, there is potential for improved performance in vision-based vehicle navigation tasks, such as trajectory planning and accident simulation, crucial for developing robust autonomous systems.

Future Prospects

Looking forward, several avenues for further inquiry emerge. Methodological enhancements could focus on accelerating the sampling process of diffusion models, aligning with techniques like progressive distillation. Moreover, integrating multi-modal data (e.g., audio or LIDAR inputs) with the FDM framework could expand its applicability in multi-sensory contexts. Connecting this work with reinforcement learning paradigms to encapsulate action and reward signals promises advancements in model-based control systems engaged in dynamic environments.

In summary, this paper contributes significantly to the potential for long-duration video generation via diffusion models, providing theoretical and empirical foundations for future AI advances in video understanding and generation realms. The methodological innovations and the release of a new benchmark set the stage for subsequent research and application-driven exploration.