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[**🌐 Homepage**](https://tiger-ai-lab.github.io/ConsistI2V/) | [**📖 arXiv**](https://arxiv.org/abs/2402.04324) | [**🖥️ Code**](https://github.com/TIGER-AI-Lab/ConsistI2V) | [**📊 I2V-Bench**](https://drive.google.com/drive/folders/1eg_vtowKZBen74W-A1oeO4bR1K21giks)
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We propose ConsistI2V, a diffusion-based method to enhance visual consistency for I2V generation. Specifically, we introduce (1) spatiotemporal attention over the first frame to maintain spatial and motion consistency, (2) noise initialization from the low-frequency band of the first frame to enhance layout consistency. These two approaches enable ConsistI2V to generate highly consistent videos.
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<img src="https://tiger-ai-lab.github.io/ConsistI2V/static/images/consisti2v_main.png" alt="ConsistI2V" style="width:
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## Environment Setup
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Prepare codebase and Conda environment using the following commands:
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[**🌐 Homepage**](https://tiger-ai-lab.github.io/ConsistI2V/) | [**📖 arXiv**](https://arxiv.org/abs/2402.04324) | [**🖥️ Code**](https://github.com/TIGER-AI-Lab/ConsistI2V) | [**📊 I2V-Bench**](https://drive.google.com/drive/folders/1eg_vtowKZBen74W-A1oeO4bR1K21giks)
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We propose ConsistI2V, a diffusion-based method to enhance visual consistency for I2V generation. Specifically, we introduce (1) spatiotemporal attention over the first frame to maintain spatial and motion consistency, (2) noise initialization from the low-frequency band of the first frame to enhance layout consistency. These two approaches enable ConsistI2V to generate highly consistent videos.
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<img src="https://tiger-ai-lab.github.io/ConsistI2V/static/images/consisti2v_main.png" alt="ConsistI2V" style="width:100%;">
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## Environment Setup
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Prepare codebase and Conda environment using the following commands:
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