Currently it requires an artist to create 3D human avatars with realistic clothing that can move naturally. Despite progress on 3D scanning and modeling of human bodies, there is still no technology that can easily turn a static scan into an animatable avatar. Automating the creation of such avatars would enable many applications in games, social networking, animation, and AR/VR to name a few. The key problem is one of representation. Standard 3D meshes are widely used in modeling the minimally-clothed body but do not readily capture the complex topology of clothing. Recent interest has shifted to implicit surface models for this task but they are computationally heavy and lack compatibility with existing 3D tools. What is needed is a 3D representation that can capture varied topology at high resolution and that can be learned from data. We argue that this representation has been with us all along -- the point cloud. Point clouds have properties of both implicit and explicit representations that we exploit to model 3D garment geometry on a human body. We train a neural network with a novel local clothing geometric feature to represent the shape of different outfits. The network is trained from 3D point clouds of many types of clothing, on many bodies, in many poses, and learns to model pose-dependent clothing deformations. The geometry feature can be optimized to fit a previously unseen scan of a person in clothing, enabling the scan to be reposed realistically. Our model demonstrates superior quantitative and qualitative results in both multi-outfit modeling and unseen outfit animation. The code is available for research purposes.
翻译:目前,它需要一位艺术家来创造3D的人性变异体,并配有可以自然移动的现实服装。尽管在3D扫描和人体建模方面取得了进展,但目前还没有任何技术可以很容易地将静态扫描转换成一个可想象的变异体。自动创建这样的变异体将使得在游戏、社交网络、动画和AR/VR中能够应用许多应用程序来举几个例子。关键的问题是代表性。标准 3D meshes被广泛用于模拟起码穿衣的体型,但不能轻易地捕捉到复杂的服装表层。最近的兴趣已经转移到隐含的表面模型上,但它们在计算上很重,与现有的3D工具不相容。需要的3D代表可以高分辨率捕捉不同的表层,从数据中可以学到。我们认为,这个代表和我们一起是点云层。点的云层具有隐含和清晰的表达特性,我们用来在人体上模型3D服装变色的模型。我们训练一个有新的当地服装变色模型网络,用来代表不同结构的变色结构的形状。 网络从许多种的变色结构,可以修整,从以前的变形到变形结构可以学习许多的变形。