用语义与几何引导生成动态环境中的自然人体动作
SemGeoMo: Dynamic Contextual Human Motion Generation with Semantic and Geometric Guidance
- 融合文本、可交互性与关节信息的多层级引导
- 在三个数据集上达当前最优，动作更合理且符合物理约束
- 适合虚拟人形机器人交互场景的动作生成
在动态环境中生成合理且高质量的人体交互动作，对理解、建模、迁移和应用人类行为至虚拟与实体机器人至关重要。本文提出一种有效方法 SemGeoMo，充分融合文本、可交互性及关节信息的多层级语义与几何引导，提升生成动作的语义合理性与几何正确性。该方法在三个数据集上达到领先性能，展现出对多样化交互场景的优异泛化能力。项目页面与代码详见 https://4dvlab.github.io/project_page/semgeomo/。
原文摘要 · Abstract (English)
Generating reasonable and high-quality human interactive motions in a given dynamic environment is crucial for understanding, modeling, transferring, and applying human behaviors to both virtual and physical robots. In this paper, we introduce an effective method, SemGeoMo, for dynamic contextual human motion generation, which fully leverages the text-affordance-joint multi-level semantic and geometric guidance in the generation process, improving the semantic rationality and geometric correctness of generative motions. Our method achieves state-of-the-art performance on three datasets and demonstrates superior generalization capability for diverse interaction scenarios. The project page and code can be found at https://4dvlab.github.io/project_page/semgeomo/.
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