Achieving realistic animated human avatars requires accurate modeling of pose-dependent clothing deformations. Existing learning-based methods heavily rely on the Linear Blend Skinning (LBS) of minimally-clothed human models like SMPL to model deformation. However, these methods struggle to handle loose clothing, such as long dresses, where the canonicalization process becomes ill-defined when the clothing is far from the body, leading to disjointed and fragmented results. To overcome this limitation, we propose a novel hybrid framework to model challenging clothed humans. Our core idea is to use dedicated strategies to model different regions, depending on whether they are close to or distant from the body. Specifically, we segment the human body into three categories: unclothed, deformed, and generated. We simply replicate unclothed regions that require no deformation. For deformed regions close to the body, we leverage LBS to handle the deformation. As for the generated regions, which correspond to loose clothing areas, we introduce a novel free-form, part-aware generator to model them, as they are less affected by movements. This free-form generation paradigm brings enhanced flexibility and expressiveness to our hybrid framework, enabling it to capture the intricate geometric details of challenging loose clothing, such as skirts and dresses. Experimental results on the benchmark dataset featuring loose clothing demonstrate that our method achieves state-of-the-art performance with superior visual fidelity and realism, particularly in the most challenging cases.

翻译：实现逼真的动画人体化身需要对姿态相关的服装变形进行精确建模。现有的基于学习的方法严重依赖线性混合蒙皮（LBS）技术，使用如SMPL等最小着装人体模型来建模变形。然而，这些方法难以处理宽松服装（如长裙），当服装远离身体时，其规范化过程变得定义不清，导致结果断裂且碎片化。为克服这一局限，我们提出了一种新颖的混合框架来建模具有挑战性的着装人体。我们的核心思想是根据区域与身体的远近，采用专用策略对不同区域进行建模。具体而言，我们将人体划分为三类：未着装区域、变形区域和生成区域。对于无需变形的未着装区域，我们直接复制。对于靠近身体的变形区域，我们利用LBS处理其变形。至于生成区域，即对应宽松服装区域，由于受运动影响较小，我们引入了一种新颖的自由形态、部件感知生成器对其进行建模。这种自由形态生成范式为我们的混合框架带来了增强的灵活性和表现力，使其能够捕捉具有挑战性的宽松服装（如裙子和连衣裙）的复杂几何细节。在包含宽松服装的基准数据集上的实验结果表明，我们的方法实现了最先进的性能，具有卓越的视觉保真度和真实感，尤其是在最具挑战性的案例中。