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