TY - GEN
T1 - Automatic Animation of Hair Blowing in Still Portrait Photos
AU - Xiao, Wenpeng
AU - Liu, Wentao
AU - Wang, Yitong
AU - Ghanem, Bernard
AU - Li, Bing
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We propose a novel approach to animate human hair in a still portrait photo. Existing work has largely studied the animation of fluid elements such as water and fire. However, hair animation for a real image remains underexplored, which is a challenging problem, due to the high complexity of hair structure and dynamics. Considering the complexity of hair structure, we innovatively treat hair wisp extraction as an instance segmentation problem, where a hair wisp is referred to as an instance. With advanced instance segmentation networks, our method extracts meaningful and natural hair wisps. Furthermore, we propose a wisp-aware animation module that animates hair wisps with pleasing motions without noticeable artifacts. The extensive experiments show the superiority of our method. Our method provides the most pleasing and compelling viewing experience in the qualitative experiments, and outperforms state-of-the-art still-image animation methods by a large margin in the quantitative evaluation. Project url: https://nevergiveu.github.io/AutomaticHairBlowing/
AB - We propose a novel approach to animate human hair in a still portrait photo. Existing work has largely studied the animation of fluid elements such as water and fire. However, hair animation for a real image remains underexplored, which is a challenging problem, due to the high complexity of hair structure and dynamics. Considering the complexity of hair structure, we innovatively treat hair wisp extraction as an instance segmentation problem, where a hair wisp is referred to as an instance. With advanced instance segmentation networks, our method extracts meaningful and natural hair wisps. Furthermore, we propose a wisp-aware animation module that animates hair wisps with pleasing motions without noticeable artifacts. The extensive experiments show the superiority of our method. Our method provides the most pleasing and compelling viewing experience in the qualitative experiments, and outperforms state-of-the-art still-image animation methods by a large margin in the quantitative evaluation. Project url: https://nevergiveu.github.io/AutomaticHairBlowing/
UR - http://www.scopus.com/inward/record.url?scp=85179159622&partnerID=8YFLogxK
U2 - 10.1109/ICCV51070.2023.02099
DO - 10.1109/ICCV51070.2023.02099
M3 - Conference contribution
AN - SCOPUS:85179159622
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 22906
EP - 22918
BT - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
Y2 - 2 October 2023 through 6 October 2023
ER -