Obtaining personalized 3D animatable avatars from a monocular camera has
several real world applications in gaming, virtual try-on, animation, and
VR/XR, etc. However, it is very challenging to model dynamic and fine-grained
clothing deformations from such sparse data. Existing methods for modeling 3D
humans from depth data have limitations in terms of computational efficiency,
mesh coherency, and flexibility in resolution and topology. For instance,
reconstructing shapes using implicit functions and extracting explicit meshes
per frame is computationally expensive and cannot ensure coherent meshes across
frames. Moreover, predicting per-vertex deformations on a pre-designed human
template with a discrete surface lacks flexibility in resolution and topology.
To overcome these limitations, we propose a novel method `\keyfeature: Neural
Surface Fields' for modeling 3D clothed humans from monocular depth. NSF
defines a neural field solely on the base surface which models a continuous and
flexible displacement field. NSF can be adapted to the base surface with
different resolution and topology without retraining at inference time.
Compared to existing approaches, our method eliminates the expensive per-frame
surface extraction while maintaining mesh coherency, and is capable of
reconstructing meshes with arbitrary resolution without retraining. To foster
research in this direction, we release our code in project page at:
this https URL

通过具有个性化、三维可动角色的单目摄像头获取数据，在建模动态、细粒度服装变形方面存在挑战。本文提出了一种名为 ' 神经表面场 ' 的新方法，用于从单目深度数据中建模穿着服装的三维人物，以超越现有方法的局限性，具有计算效率高、网格连贯性好以及能够重建任意分辨率网格等优势。