TY  - JOUR
PB  - ASSOC COMPUTING MACHINERY
UR  - https://doi.org/10.1145/3478513.3480497
TI  - Dynamic Neural Garments
AV  - public
EP  - 15
SN  - 0730-0301
KW  - Science & Technology
KW  -  Technology
KW  -  Computer Science
KW  -  Software Engineering
KW  -  Computer Science
KW  -  Neural rendering
KW  -  animation
KW  -  neural simulation
KW  -  avatars
KW  -  dynamic garments
IS  - 6
A1  - Zhang, Meng
A1  - Wang, Tuanfeng Y
A1  - Ceylan, Duygu
A1  - Mitra, Niloy J
N2  - 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 synthesize plausible dynamic garment appearance from a desired 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 demonstrate our method on a wide range of real and synthetic garments. We also provide comparisons against existing neural rendering and image sequence translation approaches, and report clear quantitative and qualitative improvements. Project page: http://geometry.cs.ucl.ac.uk/projects/2021/DynamicNeuralGarments/
Y1  - 2021/12/01/
JF  - ACM Transactions on Graphics
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
VL  - 40
ID  - discovery10159071
ER  -