Dynamic-I2V: Exploring Image-to-Video Generation Models via Multimodal LLM

• URL: http://arxiv.org/abs/2505.19901v3
• Date: Tue, 03 Jun 2025 07:32:50 GMT
• Title: Dynamic-I2V: Exploring Image-to-Video Generation Models via Multimodal LLM
• Authors: Peng Liu, Xiaoming Ren, Fengkai Liu, Qingsong Xie, Quanlong Zheng, Yanhao Zhang, Haonan Lu, Yujiu Yang,
• Abstract summary: We present Dynamic-I2V, an innovative framework that integrates Multimodal Large Language Models (MLLMs) to jointly encode visual and textual conditions for a diffusion transformer (DiT) architecture.<n>We show that Dynamic-I2V attains state-of-the-art performance in image-to-video generation, particularly revealing significant improvements of 42.5%, 7.9%, and 11.8% in dynamic range, controllability, and quality, respectively.
• Score: 34.23005193437701
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in image-to-video (I2V) generation have shown promising performance in conventional scenarios. However, these methods still encounter significant challenges when dealing with complex scenes that require a deep understanding of nuanced motion and intricate object-action relationships. To address these challenges, we present Dynamic-I2V, an innovative framework that integrates Multimodal Large Language Models (MLLMs) to jointly encode visual and textual conditions for a diffusion transformer (DiT) architecture. By leveraging the advanced multimodal understanding capabilities of MLLMs, our model significantly improves motion controllability and temporal coherence in synthesized videos. The inherent multimodality of Dynamic-I2V further enables flexible support for diverse conditional inputs, extending its applicability to various downstream generation tasks. Through systematic analysis, we identify a critical limitation in current I2V benchmarks: a significant bias towards favoring low-dynamic videos, stemming from an inadequate balance between motion complexity and visual quality metrics. To resolve this evaluation gap, we propose DIVE - a novel assessment benchmark specifically designed for comprehensive dynamic quality measurement in I2V generation. In conclusion, extensive quantitative and qualitative experiments confirm that Dynamic-I2V attains state-of-the-art performance in image-to-video generation, particularly revealing significant improvements of 42.5%, 7.9%, and 11.8% in dynamic range, controllability, and quality, respectively, as assessed by the DIVE metric in comparison to existing methods.

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