Core Concepts
TRIP introduces temporal residual learning with image noise prior for coherent image-to-video generation.
Abstract
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.
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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.
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Data Extraction
- "Extensive experiments on WebVid-10M, DTDB, and MSR-VTT datasets demonstrate the effectiveness of our TRIP for image-to-video generation."
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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.
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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.
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Deep Residual Learning
- Deep neural networks benefit from learning residual components with shortcut connections.
- Integrating residual learning enhances network optimization.
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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.
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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.
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Conclusion
- TRIP presents a novel approach for enhancing image-to-video diffusion models through temporal residual learning with image noise prior.
Stats
Extensive experiments on WebVid-10M, DTDB, and MSR-VTT datasets demonstrate the effectiveness of our TRIP for image-to-video generation.
Quotes
"Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models."
"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale."