The October 9, 2025 paper details the architecture, training, and evaluation of **UniVideo**, a unified multimodal generative system capable of **handling a wide array of image and video tasks**. UniVideo integrates a **frozen Multimodal Large Language Model (MLLM)** for understanding complex instructions and a **multimodal Diffusion Transformer (MMDiT)** for generation, connected by a trainable MLP. The system is trained across three stages, progressing from connector alignment to multi-task fine-tuning on diverse data, including text-to-image/video generation and in-context editing. Notably, UniVideo demonstrates strong **zero-shot generalization** to tasks like free-form video editing and novel task compositions, often achieving **superior or competitive mask-free performance** compared to task-specific expert models and commercial baselines like Pika2.2 and Kling1.6. Ablation studies confirm the effectiveness of the unified multi-task approach and the importance of streaming visual inputs to both the MLLM and MMDiT branches for better identity preservation.Source:https://arxiv.org/pdf/2510.08377

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