Human Motion Video Generation: A Survey

• URL: http://arxiv.org/abs/2509.03883v1
• Date: Thu, 04 Sep 2025 04:39:21 GMT
• Title: Human Motion Video Generation: A Survey
• Authors: Haiwei Xue, Xiangyang Luo, Zhanghao Hu, Xin Zhang, Xunzhi Xiang, Yuqin Dai, Jianzhuang Liu, Zhensong Zhang, Minglei Li, Jian Yang, Fei Ma, Zhiyong Wu, Changpeng Yang, Zonghong Dai, Fei Richard Yu,
• Abstract summary: This paper provides an in-depth survey of human motion video generation, encompassing over ten sub-tasks.<n>It details the five key phases of the generation process: input, motion planning, motion video generation, refinement, and output.<n> Notably, this is the first survey that discusses the potential of large language models in enhancing human motion video generation.
• Score: 65.24556163013375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human motion video generation has garnered significant research interest due to its broad applications, enabling innovations such as photorealistic singing heads or dynamic avatars that seamlessly dance to music. However, existing surveys in this field focus on individual methods, lacking a comprehensive overview of the entire generative process. This paper addresses this gap by providing an in-depth survey of human motion video generation, encompassing over ten sub-tasks, and detailing the five key phases of the generation process: input, motion planning, motion video generation, refinement, and output. Notably, this is the first survey that discusses the potential of large language models in enhancing human motion video generation. Our survey reviews the latest developments and technological trends in human motion video generation across three primary modalities: vision, text, and audio. By covering over two hundred papers, we offer a thorough overview of the field and highlight milestone works that have driven significant technological breakthroughs. Our goal for this survey is to unveil the prospects of human motion video generation and serve as a valuable resource for advancing the comprehensive applications of digital humans. A complete list of the models examined in this survey is available in Our Repository https://github.com/Winn1y/Awesome-Human-Motion-Video-Generation.

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