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Transferring Dense Pose to Proximal Animal Classes

Artsiom Sanakoyeu, Vasil Khalidov, Maureen S. McCarthy, Andrea Vedaldi, Natalia Neverova, In CVPR 2020

🌐 Project page: https://asanakoy.github.io/densepose-evolution/
📝 Paper pdf: https://arxiv.org/pdf/2003.00080.pdf

News

04.03.2021. Source code fo our method was published as a part of Detectron2 library.

Source code

DenseposeEvolution Models & Bootstrapping Pipeline

image

We introduced a pipeline to transfer DensePose models trained on humans to proximal animal classes (chimpanzees), which is summarized in Figure 3. The training proceeds in two stages:

First, a master model is trained on data from source domain (humans with full DensePose annotation S, I, U and V) and supporting domain (animals with segmentation annotation only). Only selected animal classes are chosen from the supporting domain through category filters to guarantee the quality of target domain results. The training is done in class-agnostic manner: all selected categories are mapped to a single category (human).

Second, a student model is trained on data from source and supporting domains, as well as data from target domain obtained by applying the master model, selecting high-confidence detections and sampling the results.

Examples of pretrained master and student models are available in the Model Zoo.

For more details on the bootstrapping pipeline and how to train, please see Bootstrapping Pipeline README.

References

If you use our method, please take the references from the following BibTeX entries:

DenseposeEvolution bootstrapping pipeline:

@InProceedings{Sanakoyeu2020TransferringDensePose,
    title = {Transferring Dense Pose to Proximal Animal Classes},
    author = {Artsiom Sanakoyeu and Vasil Khalidov and Maureen S. McCarthy and Andrea Vedaldi and Natalia Neverova},
    journal = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2020},
}

DensePose with confidence estimation:

@InProceedings{Neverova2019DensePoseConfidences,
    title = {Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels},
    author = {Neverova, Natalia and Novotny, David and Vedaldi, Andrea},
    journal = {Advances in Neural Information Processing Systems},
    year = {2019},
}

Original DensePose:

@InProceedings{Guler2018DensePose,
  title={DensePose: Dense Human Pose Estimation In The Wild},
  author={R\{i}za Alp G\"uler, Natalia Neverova, Iasonas Kokkinos},
  journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018}
}