让虚拟人物在复杂场景中自然互动，同时保持动作真实。
ComplexMimic: Human-Scene Interaction Imitation in Complex 3D Environments
- 双专家策略：一个模仿动作，一个处理碰撞适应
- 难样本优先训练，提升复杂行为学习效果
- 适用于需要真实物理交互的虚拟角色生成
基于物理的人-场景交互（HSI）模仿学习对具身智能至关重要，它连接了三维动作与真实世界动态。然而，现有方法多聚焦于简化场景，限制了其在真实环境中的应用。本文聚焦复杂环境下的HSI模仿。我们发现，在复杂环境中存在动作完成度与动作自然性之间的固有权衡。为此，提出ComplexMimic框架，通过解析不完美动捕数据重建多样化的交互行为。首先引入双流策略，学习两个互补专家：一个用于精确运动追踪，另一个用于复杂场景中的碰撞感知适应。其次，传统多专家蒸馏平权分配监督信号，常导致挑战性行为欠采样，影响学习效率。为此，提出一种难度感知蒸馏策略，基于失败统计和学习进度信号，自适应加权并优先学习难但可学的轨迹。在三个基准数据集上的大量实验表明，本方法优于当前最优技术。
原文摘要 · Abstract (English)
Physics-based Human-Scene Interaction (HSI) imitation learning is crucial for embodied intelligence as it bridges the gap between kinematic 3D motions and real-world dynamics. However, most existing methods focus on simplified scene settings, leaving complex environments largely unexplored, which limits their applicability in real-world scenarios. In this paper, we focus on HSI mimicry in complex environments. Under this complex setting, we observe an inherent trade-off between successfully performing interaction and maintaining natural, physically plausible motions. To address this challenge, we propose ComplexMimic, a framework that reconstructs diverse HSI by interpreting imperfect MoCap data. First, we introduce a Dual Flow Strategy, which learns two complementary experts: an imitation expert for accurate motion tracking and an interaction expert for collision-aware adaptation in complex scenes. Second, naive multi-expert distillation, which treats all experts equally, often under-samples challenging behaviors, limiting effective learning. To mitigate this issue, we propose a difficulty-aware distillation strategy that adaptively weights supervision and prioritizes hard-yet-learnable trajectories guided by failure statistics and learning progress signals. Extensive experiments on three benchmark datasets demonstrate that our approach outperforms current state-of-the-art methods.
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