Diffusion Probabilistic Modeling for Video Generation (2203.09481v5)

Abstract: Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in perceptual and probabilistic forecasting metrics. We propose an autoregressive, end-to-end optimized video diffusion model inspired by recent advances in neural video compression. The model successively generates future frames by correcting a deterministic next-frame prediction using a stochastic residual generated by an inverse diffusion process. We compare this approach against five baselines on four datasets involving natural and simulation-based videos. We find significant improvements in terms of perceptual quality for all datasets. Furthermore, by introducing a scalable version of the Continuous Ranked Probability Score (CRPS) applicable to video, we show that our model also outperforms existing approaches in their probabilistic frame forecasting ability.

Diffusion Probabilistic Modeling for Video Generation

The paper "Diffusion Probabilistic Modeling for Video Generation" presents an advanced approach to video generation using diffusion probabilistic models, an extension of diffusion models that have been previously successful in high-quality image generation contexts. The research specifically focuses on generating video frames by predicting residuals to a deterministic prediction of the next frame, thereby enhancing the quality of the generated videos.

Key Contributions

The authors propose an autoregressive, neural network-based model for video generation that integrates stochastic residuals using an inverse diffusion process. This approach is inspired by predictive coding principles and neural compression algorithms, which emphasize the efficiency of modeling residual errors over dense observations. The paper's main contributions include:

1. Application of Diffusion Models to Video: The research extends diffusion probabilistic models typically used in image generation to video generation, showing that such models can outperform existing methods both in terms of perceptual quality metrics and probabilistic forecasting abilities.
2. Novel Evaluation Metric: A scalable adaptation of the Continuous Ranked Probability Score (CRPS) is introduced for evaluating the probabilistic frame forecasting capability of video generation models. This provides a quantitative measure of distributional agreement between model predictions and actual data.
3. Empirical Validation: The model demonstrates substantial improvements over five baseline methods on four datasets, showcasing significant gains in perceptual quality across different video types and complexity levels.

Methodology

The proposed method involves two main steps: a deterministic prediction step that uses convolutional RNNs to predict the next video frame, and a stochastic correction step that predicts a residual using a denoising diffusion process. The combination of these steps enables the model to handle the inherent multi-modality and stochastic nature of future video predictions, facilitating high-resolution content generation without introducing blurry artifacts.

The paper describes a structured process for the generative and inference steps of the diffusion models, laying out how latent variables are used in conjunction with observed frames to direct the model's learning process. Stochastic gradient descent is employed for optimizing the model's parameters, and a carefully structured training algorithm ensures that the video frames are generated with high fidelity.

Results and Implications

The authors show that the Residual Video Diffusion (RVD) model provides state-of-the-art performance in terms of perceptual quality, as indicated by metrics such as Fréchet Video Distance (FVD) and Learned Perceptual Image Patch Similarity (LPIPS). Additionally, the application of CRPS allows them to objectively compare probabilistic predictions across various models. Consistently, RVD demonstrates superiority in predictive accuracy on high-resolution datasets and delivers competitive performance on simpler datasets.

The implications of this research are significant for the fields of adaptive video streaming, model-based reinforcement learning, and potentially neural video compression. By producing high-quality video frames, these models can contribute to improved visual realism in synthetic video scenarios, enhancing applications ranging from digital content creation to autonomous driving simulations.

Future Directions

Building on the robust performance and novel methodologies introduced, future research can explore scaling these models to encompass more diverse and complex datasets, including those with abrupt discontinuities in scene dynamics. Additionally, optimizing the computational cost and inference speed of diffusion-based video models could broaden their applicability in real-time applications. Integrating such models with complementary AI technologies could further enhance their utility in a wide array of data-driven disciplines.

In summary, the advancement of diffusion probabilistic modeling for video generation as presented in this paper signals a meaningful forward step in generative modeling, setting a high benchmark for future explorations in this rapidly evolving field.