MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation (2205.09853v4)

Abstract: Video prediction is a challenging task. The quality of video frames from current state-of-the-art (SOTA) generative models tends to be poor and generalization beyond the training data is difficult. Furthermore, existing prediction frameworks are typically not capable of simultaneously handling other video-related tasks such as unconditional generation or interpolation. In this work, we devise a general-purpose framework called Masked Conditional Video Diffusion (MCVD) for all of these video synthesis tasks using a probabilistic conditional score-based denoising diffusion model, conditioned on past and/or future frames. We train the model in a manner where we randomly and independently mask all the past frames or all the future frames. This novel but straightforward setup allows us to train a single model that is capable of executing a broad range of video tasks, specifically: future/past prediction -- when only future/past frames are masked; unconditional generation -- when both past and future frames are masked; and interpolation -- when neither past nor future frames are masked. Our experiments show that this approach can generate high-quality frames for diverse types of videos. Our MCVD models are built from simple non-recurrent 2D-convolutional architectures, conditioning on blocks of frames and generating blocks of frames. We generate videos of arbitrary lengths autoregressively in a block-wise manner. Our approach yields SOTA results across standard video prediction and interpolation benchmarks, with computation times for training models measured in 1-12 days using $\le$ 4 GPUs. Project page: https://mask-cond-video-diffusion.github.io ; Code : https://github.com/voletiv/mcvd-pytorch

Overview of "MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation"

The paper "MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation" introduces a novel approach to handling various video synthesis tasks using a unified framework. The approach, known as Masked Conditional Video Diffusion (MCVD), leverages probabilistic conditional score-based denoising diffusion models, conditioned on past and/or future video frames. The authors develop a singular model capable of executing future video prediction, past prediction, unconditional video generation, and interpolation tasks by innovatively employing masking strategies during training.

Methodology

At the core of MCVD lies a diffusion model that utilizes a conditional score-based denoising process. This process is trained to predict noise as part of a forward diffusion process that corrupts input video frames. To facilitate a wide array of tasks, MCVD introduces a random masking mechanism. During training, either all past frames, all future frames, or both are masked. This mechanism compels the network to learn implicit dynamics of the video content that are crucial across a range of tasks:

1. Video Prediction: MCVD predicts future frames by masking available future frames during training.
2. Past Prediction: Similar to video prediction, this task involves predicting past frames by masking existing past frames during training.
3. Unconditional Generation: By masking all frames (both past and future), the model learns to generate video sequences from noise unconditionally.
4. Interpolation: With no frames masked, the model learns to interpolate missing frames given both past and future sequences.

The architecture employs non-recurrent 2D-convolutional blocks along with convolutional U-net structures, allowing the model to condition on blocks of frames and generate videos in a block-wise manner autoregressively. SPAce-TIme-Adaptive Normalization (SPATIN) is introduced to integrate conditional information, enhancing the adaptability of the network to various synthesis tasks.

Results and Performance

The MCVD framework was tested across multiple standard datasets such as Stochastic Moving MNIST, KTH, BAIR, and Cityscapes. It achieved state-of-the-art (SOTA) results in frame prediction and interpolation benchmarks. Notably, it shows substantial improvements in FVD (Frechet Video Distance) scores, a metric aligning closely with perceptual video quality and diversity, outperforming previous models which rely on considerably larger architectures and more computational resources.

Implications and Future Directions

MCVD's ability to leverage a singular architecture with masking to generalize across multiple video-related tasks presents significant implications for the field of video processing and synthesis. Practically, it allows for efficient resource usage while delivering high-quality outputs suitable for applications ranging from autonomous vehicles to cinematic special effects. Theoretically, it sets a new precedent in designing models that can handle varied tasks without task-specific architecture modulation, pointing towards a future where generalizable, resource-efficient video synthesis models become more prevalent.

Future research could focus on scaling these models to handle higher resolution and longer-duration videos, while also enhancing the speed of the reverse diffusion process, which is a known bottleneck. Additionally, the exploration of alternative conditioning mechanisms or further integration with learned representations elucidated in text-based or multimodal contexts could expand the applicability of MCVD-style architectures.

Overall, the paper presents a substantial contribution by redefining how video generation tasks can be approached via diffusion models, offering pathways for more cohesive, adaptable approaches to video synthesis.