Motion-2-to-3: Leveraging 2D Motion Data to Boost 3D Motion Generation

• URL: http://arxiv.org/abs/2412.13111v1
• Date: Tue, 17 Dec 2024 17:34:52 GMT
• Title: Motion-2-to-3: Leveraging 2D Motion Data to Boost 3D Motion Generation
• Authors: Huaijin Pi, Ruoxi Guo, Zehong Shen, Qing Shuai, Zechen Hu, Zhumei Wang, Yajiao Dong, Ruizhen Hu, Taku Komura, Sida Peng, Xiaowei Zhou,
• Abstract summary: 2D human videos offer a vast and accessible source of motion data, covering a wider range of styles and activities. We introduce a novel framework that disentangles local joint motion from global movements, enabling efficient learning of local motion priors from 2D data. Our method efficiently utilizes 2D data, supporting realistic 3D human motion generation and broadening the range of motion types it supports.
• Score: 43.915871360698546
• License:
• Abstract: Text-driven human motion synthesis is capturing significant attention for its ability to effortlessly generate intricate movements from abstract text cues, showcasing its potential for revolutionizing motion design not only in film narratives but also in virtual reality experiences and computer game development. Existing methods often rely on 3D motion capture data, which require special setups resulting in higher costs for data acquisition, ultimately limiting the diversity and scope of human motion. In contrast, 2D human videos offer a vast and accessible source of motion data, covering a wider range of styles and activities. In this paper, we explore leveraging 2D human motion extracted from videos as an alternative data source to improve text-driven 3D motion generation. Our approach introduces a novel framework that disentangles local joint motion from global movements, enabling efficient learning of local motion priors from 2D data. We first train a single-view 2D local motion generator on a large dataset of text-motion pairs. To enhance this model to synthesize 3D motion, we fine-tune the generator with 3D data, transforming it into a multi-view generator that predicts view-consistent local joint motion and root dynamics. Experiments on the HumanML3D dataset and novel text prompts demonstrate that our method efficiently utilizes 2D data, supporting realistic 3D human motion generation and broadening the range of motion types it supports. Our code will be made publicly available at https://zju3dv.github.io/Motion-2-to-3/.

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