LaxMotion: 重新思考3D人体运动生成的监督粒度
LaxMotion: Rethinking Supervision Granularity for 3D Human Motion Generation
AI总结：
LaxMotion通过放松监督粒度，以结构一致性代替精确坐标匹配，实现更高效且稳健的3D人体运动生成。
摘要：
最近的3D人体运动生成模型表现出显著的重建准确性，但在泛化到训练分布之外时却遇到困难。这种限制部分源于使用精确的3D监督，这促使模型拟合固定的坐标模式，而不是学习必要的3D结构和运动语义线索，以实现稳健的泛化。为克服这一限制，我们提出了LaxMotion，一个框架，它合成逼真的3D运动，而无需直接的3D姿态监督。与其回归到精确坐标，LaxMotion学习3D运动作为全局轨迹和单目2D运动线索的一致解释。我们引入了结构化的运动因子分解，以及在放松可观测性下的重新表述训练范式。这种设计进一步通过放松的正则化目标来支持，这些目标强制视图一致的对齐、方向一致性和结构稳定性。在这一放松的监督范式下，LaxMotion生成多样化、时间一致且语义对齐的3D运动，其性能与或优于完全3D监督方法。这些结果表明，将监督从精确坐标匹配转向结构一致性促进了更强的推理和改进的泛化，提供了一种可扩展且数据高效的3D运动生成范式。
英文摘要：
Recent 3D human motion generation models demonstrate remarkable reconstruction accuracy yet struggle to generalize beyond training distributions. This limitation arises partly from the use of precise 3D supervision, which encourages models to fit fixed coordinate patterns instead of learning the essential 3D structure and motion semantic cues required for robust generalization. To overcome this limitation, we propose LaxMotion, a framework that synthesizes realistic 3D motions without direct 3D pose supervision. Instead of regressing toward exact coordinates, LaxMotion learns 3D motion as a consistent explanation of global trajectories and monocular 2D kinematic cues. We introduce a structured motion factorization together with a reformulated training paradigm under relaxed observability. This design is further supported by relaxed regularization objectives that enforce view consistent alignment, orientation coherence, and structural stability. Under this relaxed supervision paradigm, LaxMotion generates diverse, temporally coherent, and semantically aligned 3D motions, achieving performance comparable to or surpassing fully 3D supervised methods. These results indicate that shifting supervision from exact coordinate matching to structural consistency promotes stronger reasoning and improved generalization, offering a scalable and data efficient paradigm for 3D motion generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
