Skeleton-Aware Networks for Deep Motion Retargeting

@article{Aberman2020SkeletonAwareNF,
  title={Skeleton-Aware Networks for Deep Motion Retargeting},
  author={Kfir Aberman and Peizhuo Li and Dani Lischinski and Olga Sorkine-Hornung and Daniel Cohen-Or and Baoquan Chen},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.05732}
}
  • Kfir Aberman, Peizhuo Li, +3 authors Baoquan Chen
  • Published 2020
  • Computer Science
  • ArXiv
  • We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Importantly, our approach learns how to retarget without requiring any explicit pairing between the motions in the training set. We leverage the fact that different homeomorphic skeletons may be reduced to a common primal skeleton by a sequence of edge merging operations, which we refer to as skeletal pooling. Thus, our… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 39 REFERENCES

    Neural Kinematic Networks for Unsupervised Motion Retargetting

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Optimizing Network Structure for 3D Human Pose Estimation

    Structural-RNN: Deep Learning on Spatio-Temporal Graphs

    VIEW 1 EXCERPT

    Motion Style Retargeting to Characters With Different Morphologies

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL