Pose2Pose: Pose Selection and Transfer for 2D Character Animation

被引:13
|
作者
Willett, Nora S. [1 ,2 ]
Shin, Hijung Valentina [3 ]
Jin, Zeyu [3 ]
Li, Wilmot [3 ]
Finkelstein, Adam [1 ]
机构
[1] Princeton Univ, Princeton, NJ 08544 USA
[2] Pixar Animat Studios, Emeryville, CA 94608 USA
[3] Adobe Res, San Francisco, CA USA
关键词
Pose selection; animation; 2D character creation;
D O I
10.1145/3377325.3377505
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
An artist faces two challenges when creating a 2D animated character to mimic a specific human performance. First, the artist must design and draw a collection of artwork depicting portions of the character in a suitable set of poses, for example arm and hand poses that can be selected and combined to express the range of gestures typical for that person. Next, to depict a specific performance, the artist must select and position the appropriate set of artwork at each moment of the animation. This paper presents a system that addresses these challenges by leveraging video of the target human performer. Our system tracks arm and hand poses in an example video of the target. The UI displays clusters of these poses to help artists select representative poses that capture the actor's style and personality. From this mapping of pose data to character artwork, our system can generate an animation from a new performance video. It relies on a dynamic programming algorithm to optimize for smooth animations that match the poses found in the video. Artists used our system to create four 2D characters and were pleased with the final automatically animated results. We also describe additional applications addressing audio-driven or text-based animations.
引用
收藏
页码:88 / 99
页数:12
相关论文
共 50 条
  • [21] A lightweight 2D Pose Machine with attention enhancement
    Schirmer, Luiz
    Lucio, Djalma
    Raposo, Alberto
    Velho, Luiz
    Lopes, Helio
    2020 33RD SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2020), 2020, : 324 - 331
  • [22] 2D Human Pose Estimation in TV Shows
    Ferrari, Vittorio
    Marin-Jimenez, Manuel
    Zisserman, Andrew
    STATISTICAL AND GEOMETRICAL APPROACHES TO VISUAL MOTION ANALYSIS, 2009, 5604 : 128 - +
  • [23] 2D Methods for Pose Invariant Face Recognition
    Mokoena, Nthabiseng
    Tsague, Hippolyte Djonon
    Helberg, Albert
    2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE & COMPUTATIONAL INTELLIGENCE (CSCI), 2016, : 841 - 846
  • [24] Efficient Sparse Pose Adjustment for 2D Mapping
    Konolige, Kurt
    Grisetti, Giorgio
    Kuemmerle, Rainer
    Limketkai, Benson
    Vincent, Regis
    IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010,
  • [25] The Progress of Human Pose Estimation: A Survey and Taxonomy of Models Applied in 2D Human Pose Estimation
    Munea, Tewodros Legesse
    Jembre, Yalew Zelalem
    Weldegebriel, Halefom Tekle
    Chen, Longbiao
    Huang, Chenxi
    Yang, Chenhui
    IEEE ACCESS, 2020, 8 : 133330 - 133348
  • [26] 3D Excavator Pose Estimation: Direct Optimization from 2D Pose Using Kinematic Constraints
    Wen, Leyang
    Kim, Daeho
    Liu, Meiyin
    Lee, SangHyun
    COMPUTING IN CIVIL ENGINEERING 2021, 2022, : 967 - 975
  • [27] Mask-Pose Cascaded CNN for 2D Hand Pose Estimation From Single Color Image
    Wang, Yangang
    Peng, Cong
    Liu, Yebin
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2019, 29 (11) : 3258 - 3268
  • [28] Interact-Pose Datasets for 2D Human Pose Estimation in Multi-person Interaction Scene
    Jiang, Yifei
    Gao, Hao
    ARTIFICIAL INTELLIGENCE AND ROBOTICS, ISAIR 2022, PT II, 2022, 1701 : 211 - 223
  • [29] KSL-POSE: A Real-Time 2D Human Pose Estimation Method Based on Modified YOLOv8-Pose Framework
    Lu, Tianyi
    Cheng, Ke
    Hua, Xuecheng
    Qin, Suning
    SENSORS, 2024, 24 (19)
  • [30] Model transfer from 2D to 3D study for boxing pose estimation
    Lin, Jianchu
    Xie, Xiaolong
    Wu, Wangping
    Xu, Shengpeng
    Liu, Chunyan
    Hudoyberdi, Toshboev
    Chen, Xiaobing
    FRONTIERS IN NEUROROBOTICS, 2023, 17