Video Interpolation with Diffusion Models: Generating High-Quality Intermediate Frames Between Input Frames

מושגי ליבה

VIDIM, a generative model for video interpolation, creates short videos given a start and end frame by using cascaded diffusion models to generate the target video at low resolution and then at high resolution, enabling high-fidelity results even for complex, nonlinear, or ambiguous motions.

תקציר

The paper presents VIDIM, a generative model for video interpolation that uses cascaded diffusion models to generate high-quality intermediate frames between two input frames.

Key highlights:

• VIDIM first generates a low-resolution video between the input frames using a base diffusion model, and then generates the high-resolution video conditioned on the low-resolution output using a super-resolution diffusion model.
• This cascaded approach allows VIDIM to handle complex, nonlinear, or ambiguous motions that pose challenges for previous state-of-the-art video interpolation methods.
• VIDIM also leverages classifier-free guidance on the input frames and parameter-free conditioning on the high-resolution input frames to further improve sample quality.
• The authors create two curated datasets, Davis-7 and UCF101-7, with large and ambiguous motions to evaluate VIDIM and other baselines.
• Quantitative and qualitative results show that VIDIM outperforms previous methods, especially on the more challenging examples, and is strongly preferred by human raters.
• The authors also demonstrate the scalability of VIDIM by increasing the model size and showing continued improvements in sample quality.

סטטיסטיקה

"We train all VIDIM models with the Adam optimizer [23] with a learning rate of 5e-4 (with linear warm-up for the first 10,000 steps) and β1 = .9, β2 = .999, gradient clipping at norm 1, and maintaining an EMA of the model parameters with decay rate .9999 following Ho et al. [15]."
"All super-resolution models were trained with noise conditioning augmentation [16] on the low-resolution frames, where we re-use the noise schedule and add noise to these frames with t ∈U(0, 0.5) for each training example."

ציטוטים

"We show that diffusion based generative models can overcome the limitations of prior state-of-the-art models for video interpolation."
"We develop a cascaded video interpolation diffusion model, which we dub VIDIM, capable of generating high-quality videos in between two input frames."
"We show that VIDIM generally achieves better results compared to prior state-of-the-art in these difficult interpolation problems across generative modeling metrics."

תובנות מפתח מזוקקות מ:

Video Interpolation with Diffusion Models

by Sidd... ב- arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01203.pdf

שאלות מעמיקות

How could VIDIM be extended to handle video extrapolation, where the model generates frames beyond the input start and end frames?

To extend VIDIM for video extrapolation, the model would need to be trained to predict frames beyond the input start and end frames. This would involve modifying the architecture to generate frames in a sequential manner, moving both forward and backward in time from the input frames. The training process would need to incorporate the concept of temporal consistency to ensure smooth transitions between frames. Additionally, the model may need to be conditioned on a larger context window to accurately predict frames further away from the input frames. By adjusting the training data and loss functions, VIDIM could learn to generate frames beyond the input frames, effectively handling video extrapolation tasks.

What other video generation tasks could benefit from the cascaded diffusion modeling approach used in VIDIM, and how would the architecture and training need to be adapted?

The cascaded diffusion modeling approach used in VIDIM could benefit tasks such as video frame expansion, video restoration, and video super-resolution. For video frame expansion, the model would need to be trained to generate additional frames between existing frames, similar to video interpolation but with a larger number of frames. Video restoration tasks could involve removing noise, artifacts, or enhancing the quality of degraded videos. Video super-resolution would require the model to generate high-resolution frames from low-resolution inputs.
To adapt the architecture and training for these tasks, the model may need to be modified to handle different input resolutions and output sizes. The training data would need to be curated to include examples relevant to the specific task, such as degraded videos for restoration or low-resolution videos for super-resolution. The loss functions and training objectives would also need to be adjusted to prioritize the desired output quality, whether it is enhanced visual fidelity, noise reduction, or increased resolution.

Could the parameter-free conditioning on input frames used in VIDIM be applied to other conditional diffusion models beyond video interpolation, and what are the potential benefits?

The parameter-free conditioning on input frames used in VIDIM could be applied to other conditional diffusion models beyond video interpolation, such as image generation, text-to-image synthesis, or image restoration. By conditioning the model on input frames without introducing additional parameters, the model can effectively leverage the information in the input frames to guide the generation process. This approach can lead to more coherent and contextually relevant outputs, as the model learns to incorporate information from the input frames without introducing unnecessary complexity.
The potential benefits of parameter-free conditioning include improved sample quality, reduced model complexity, and better generalization to unseen data. By directly feeding the input frames into the model without additional parameters, the model can focus on learning the relationships between the input and output frames, leading to more accurate and realistic generation results. Additionally, parameter-free conditioning can simplify the model architecture and training process, making it easier to interpret and optimize the model for different tasks.