OmniV2V: Versatile Video Generation and Editing via Dynamic Content Manipulation

• URL: http://arxiv.org/abs/2506.01801v1
• Date: Mon, 02 Jun 2025 15:42:06 GMT
• Title: OmniV2V: Versatile Video Generation and Editing via Dynamic Content Manipulation
• Authors: Sen Liang, Zhentao Yu, Zhengguang Zhou, Teng Hu, Hongmei Wang, Yi Chen, Qin Lin, Yuan Zhou, Xin Li, Qinglin Lu, Zhibo Chen,
• Abstract summary: We propose OmniV2V, a video model capable of generating and editing videos across different scenarios based on various operations.<n>In addition, we design a visual-text instruction module based on LLaVA, enabling the model to effectively understand the correspondence between visual content and instructions.<n>Experiments show that OmniV2V works as well as, and sometimes better than, the best existing open-source and commercial models for many video generation and editing tasks.
• Score: 22.970558073760433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of Diffusion Transformers (DiT) has brought significant advancements to video generation, especially in text-to-video and image-to-video tasks. Although video generation is widely applied in various fields, most existing models are limited to single scenarios and cannot perform diverse video generation and editing through dynamic content manipulation. We propose OmniV2V, a video model capable of generating and editing videos across different scenarios based on various operations, including: object movement, object addition, mask-guided video edit, try-on, inpainting, outpainting, human animation, and controllable character video synthesis. We explore a unified dynamic content manipulation injection module, which effectively integrates the requirements of the above tasks. In addition, we design a visual-text instruction module based on LLaVA, enabling the model to effectively understand the correspondence between visual content and instructions. Furthermore, we build a comprehensive multi-task data processing system. Since there is data overlap among various tasks, this system can efficiently provide data augmentation. Using this system, we construct a multi-type, multi-scenario OmniV2V dataset and its corresponding OmniV2V-Test benchmark. Extensive experiments show that OmniV2V works as well as, and sometimes better than, the best existing open-source and commercial models for many video generation and editing tasks.

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