Temporal Residual Learning for Image-to-Video Diffusion Models

Kernekoncepter

TRIP introduces temporal residual learning with image noise prior for coherent image-to-video generation.

Resumé

The article introduces TRIP, a novel approach for image-to-video diffusion models. It focuses on aligning frames with given images and ensuring temporal coherence. TRIP utilizes image noise prior and residual noise prediction to enhance video generation. Experiments on various datasets validate the effectiveness of TRIP in improving temporal consistency and visual quality.

1. Introduction

   • Recent advances in text-to-video generation highlight the importance of powerful diffusion models.
   • The challenge lies in animating static images while maintaining alignment and temporal coherence among frames.

2. Data Extraction

   • "Extensive experiments on WebVid-10M, DTDB, and MSR-VTT datasets demonstrate the effectiveness of our TRIP for image-to-video generation."

3. Related Work

   • Generative Adversarial Networks (GANs) and diffusion models have significantly improved generative tasks in computer vision.
   • Text-to-video diffusion models have emerged to synthesize videos based on text prompts.

4. Image-to-Video Generation

   • I2V approaches can be stochastic or conditional based on motion cues availability.
   • Conventional techniques focus on short motions like fluid elements or human poses.

5. Deep Residual Learning

   • Deep neural networks benefit from learning residual components with shortcut connections.
   • Integrating residual learning enhances network optimization.

6. Experiments

   • TRIP outperforms existing baselines in terms of F-Consistency and FVD metrics across different datasets.
   • Human evaluation confirms TRIP's superiority in temporal coherence, motion fidelity, and visual quality.

7. Application: Customized Image Animation

   • TRIP is applied to text-to-video synthesis using Stable-Diffusion XL for image synthesis.
   • Image editing models like InstructPix2Pix and ControlNet further enhance video generation based on edited images.

8. Conclusion

   • TRIP presents a novel approach for enhancing image-to-video diffusion models through temporal residual learning with image noise prior.

Statistik

Extensive experiments on WebVid-10M, DTDB, and MSR-VTT datasets demonstrate the effectiveness of our TRIP for image-to-video generation.

Citater

"Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models."
"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale."

Vigtigste indsigter udtrukket fra

TRIP

by Zhongwei Zha... kl. arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.17005.pdf

Dybere Forespørgsler

How does leveraging image noise prior improve the alignment between frames

Leveraging image noise prior improves the alignment between frames in Image-to-Video (I2V) diffusion models by providing a reference point for each frame's noise prediction. The image noise prior captures the inherent correlation between the given first frame and subsequent frames, allowing for a more faithful representation of visual content across all frames. By calculating the image noise prior based on both the input image and the latent codes of the video frames, it guides the model to amplify this alignment during synthesis. This ensures that each generated frame maintains consistency with the initial static image while also reflecting temporal coherence among adjacent frames.

What are the implications of integrating residual-like noise prediction in I2V diffusion models

Integrating residual-like noise prediction in I2V diffusion models has several implications for enhancing video generation quality. Firstly, it enables more effective modeling of temporal coherence among adjacent frames by incorporating residual information into the noise estimation process. This approach helps capture subtle variations and details in motion dynamics that may not be fully captured by traditional methods. Additionally, by combining reference noise from image priors with estimated residual noises, it allows for a more nuanced understanding of how each frame relates to its preceding context, leading to smoother transitions and improved overall video quality.

How can the concept of temporal residual learning be applied to other areas beyond image-to-video generation

The concept of temporal residual learning can be applied beyond image-to-video generation to various other areas where sequential data processing is involved. For instance:

Speech Recognition: In speech recognition tasks, leveraging temporal residuals can help improve accuracy by capturing finer details in audio sequences.

Natural Language Processing: Applying temporal residuals in language modeling tasks can enhance contextual understanding and improve predictive capabilities.

Financial Forecasting: Utilizing temporal residuals in financial forecasting models can lead to better predictions by capturing trends and patterns over time.

Healthcare Monitoring: Incorporating temporal residuals into healthcare monitoring systems can aid in detecting anomalies or changes in patient data over time for early intervention.

By integrating residual-like learning techniques across these domains, models can benefit from enhanced performance through improved sequence modeling and better adaptation to dynamic patterns within sequential data streams.