  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.
