UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation

• URL: http://arxiv.org/abs/2512.07831v1
• Date: Mon, 08 Dec 2025 18:59:01 GMT
• Title: UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation
• Authors: Jiehui Huang, Yuechen Zhang, Xu He, Yuan Gao, Zhi Cen, Bin Xia, Yan Zhou, Xin Tao, Pengfei Wan, Jiaya Jia,
• Abstract summary: We introduce UnityVideo, a unified framework for world-aware video generation.<n>Our approach features two core components: (1) dynamic noising to unify heterogeneous training paradigms, and (2) a modality switcher with an in-context learner.<n>We demonstrate that UnityVideo achieves superior video quality, consistency, and improved alignment with physical world constraints.
• Score: 61.98887854225878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent video generation models demonstrate impressive synthesis capabilities but remain limited by single-modality conditioning, constraining their holistic world understanding. This stems from insufficient cross-modal interaction and limited modal diversity for comprehensive world knowledge representation. To address these limitations, we introduce UnityVideo, a unified framework for world-aware video generation that jointly learns across multiple modalities (segmentation masks, human skeletons, DensePose, optical flow, and depth maps) and training paradigms. Our approach features two core components: (1) dynamic noising to unify heterogeneous training paradigms, and (2) a modality switcher with an in-context learner that enables unified processing via modular parameters and contextual learning. We contribute a large-scale unified dataset with 1.3M samples. Through joint optimization, UnityVideo accelerates convergence and significantly enhances zero-shot generalization to unseen data. We demonstrate that UnityVideo achieves superior video quality, consistency, and improved alignment with physical world constraints. Code and data can be found at: https://github.com/dvlab-research/UnityVideo

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