DeepCloth: Neural Garment Representation for Shape and Style Editing
DeepCloth: Neural Garment Representation for Shape and Style Editing
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DOI：
10.1109/tpami.2022.3168569
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发表时间：
2020-11
影响因子：
23.6
通讯作者：
Zhaoqi Su;Tao Yu-;Yangang Wang;Yipeng Li;Yebin Liu
中科院分区：
文献类型：
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作者：
Zhaoqi Su;Tao Yu-;Yangang Wang;Yipeng Li;Yebin Liu
服装表示、编辑和动画是计算机视觉和图形领域具有挑战性的主题。现有的服装表示仍然很难在不同形状和拓扑之间实现平滑且合理的过渡。在这项工作中，我们介绍了 DeepCloth，一个用于服装表示、重建、动画和编辑的统一框架。我们的统一框架包含 3 个组件：首先，我们用“拓扑感知 UV 位置图”表示服装几何形状，通过为 UV 位置图引入额外的拓扑感知 UV 掩模，可以统一描述具有不同形状和拓扑的各种服装。其次，为了进一步实现服装重建和编辑，我们提供了一种将基于 UV 的表示嵌入到连续特征空间中的方法，该方法分别通过潜在空间中的优化和控制来实现服装形状重建和编辑。最后，我们提出了一种服装动画方法，通过将我们的神经服装表示与身体形状和姿势相结合，即使在激烈的服装编辑操作下，也可以利用我们的形状和风格表示编码的动态信息来实现合理的服装动画结果。总而言之，通过 DeepCloth，我们在建立更灵活、更通用的 3D 服装数字化框架方面向前迈进了一步。实验表明，与以前的方法相比，我们的方法可以实现最先进的服装表示性能。
Garment representation, editing and animation are challenging topics in the area of computer vision and graphics. It remains difficult for existing garment representations to achieve smooth and plausible transitions between different shapes and topologies. In this work, we introduce, DeepCloth, a unified framework for garment representation, reconstruction, animation and editing. Our unified framework contains 3 components: First, we represent the garment geometry with a “topology-aware UV-position map”, which allows for the unified description of various garments with different shapes and topologies by introducing an additional topology-aware UV-mask for the UV-position map. Second, to further enable garment reconstruction and editing, we contribute a method to embed the UV-based representations into a continuous feature space, which enables garment shape reconstruction and editing by optimization and control in the latent space, respectively. Finally, we propose a garment animation method by unifying our neural garment representation with body shape and pose, which achieves plausible garment animation results leveraging the dynamic information encoded by our shape and style representation, even under drastic garment editing operations. To conclude, with DeepCloth, we move a step forward in establishing a more flexible and general 3D garment digitization framework. Experiments demonstrate that our method can achieve state-of-the-art garment representation performance compared with previous methods.