A vital task of the wider digital human effort is the creation of realistic
garments on digital avatars, both in the form of characteristic fold patterns
and wrinkles in static frames as well as richness of garment dynamics under
avatars' motion. Existing workflow of modeling, simulation, and rendering
closely replicates the physics behind real garments, but is tedious and
requires repeating most of the workflow under changes to characters' motion,
camera angle, or garment resizing. Although data-driven solutions exist, they
either focus on static scenarios or only handle dynamics of tight garments. We
present a solution that, at test time, takes in body joint motion to directly
produce realistic dynamic garment image sequences. Specifically, given the
target joint motion sequence of an avatar, we propose dynamic neural garments
to jointly simulate and render plausible dynamic garment appearance from an
unseen viewpoint. Technically, our solution generates a coarse garment proxy
sequence, learns deep dynamic features attached to this template, and neurally
renders the features to produce appearance changes such as folds, wrinkles, and
silhouettes. We demonstrate generalization behavior to both unseen motion and
unseen camera views. Further, our network can be fine-tuned to adopt to new
body shape and/or background images. We also provide comparisons against
existing neural rendering and image sequence translation approaches, and report
clear quantitative improvements.

本文介绍了一种解决数字化人物着装的解决方案，该方案可以通过神经网络在测试时直接生成逼真的动态服装图像序列，以解决现有工作流程中重复性高且耗时的问题，并且可以在新的身体形状和背景图像下进行微调，并提供了与现有神经渲染和图像序列转换方法的定量比较。