论文标题
Autoavatar：动态化头像建模的自回旋神经领域
AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling
论文作者
论文摘要
诸如隐式表面之类的神经场最近通过无明确的时间对应关系实现了从原始扫描进行的化身建模。在这项工作中，我们利用自回归建模来进一步扩展此概念以捕获动态效应，例如软组织变形。尽管自回归模型自然能够处理动力学，但是将它们应用于隐式表示是不平凡的，因为由于内存的过度要求，明确的状态解码是不可行的。在这项工作中，我们首次实现了隐式化身的自回归建模。为了减少记忆瓶颈并有效地对动态隐式表面进行建模，我们介绍了铰接观察者点的概念，该观察者点将隐式状态与参数人体模型的明确表面相关联。我们证明，与潜在表示相比，将隐式表面编码为在铰接观察者点上定义的一组高度场，从而使概括明显更好。实验表明，我们的方法的表现超过了艺术的状态，即使是看不见的动作，也达到了合理的动态变形。 https://zqbai-jeremy.github.io/autoavatar
Neural fields such as implicit surfaces have recently enabled avatar modeling from raw scans without explicit temporal correspondences. In this work, we exploit autoregressive modeling to further extend this notion to capture dynamic effects, such as soft-tissue deformations. Although autoregressive models are naturally capable of handling dynamics, it is non-trivial to apply them to implicit representations, as explicit state decoding is infeasible due to prohibitive memory requirements. In this work, for the first time, we enable autoregressive modeling of implicit avatars. To reduce the memory bottleneck and efficiently model dynamic implicit surfaces, we introduce the notion of articulated observer points, which relate implicit states to the explicit surface of a parametric human body model. We demonstrate that encoding implicit surfaces as a set of height fields defined on articulated observer points leads to significantly better generalization compared to a latent representation. The experiments show that our approach outperforms the state of the art, achieving plausible dynamic deformations even for unseen motions. https://zqbai-jeremy.github.io/autoavatar
