Abstract
This work addresses the problem of the animal shape and pose estimation from an occluded image. Most of exisiting 3D animal reconstruction methods focus on an automatic and accurate framework in normal conditions, but ignore some exceptional occasions, such as occlusion, which limits the practical applications of estimating the animal shape and pose to a large extent. In this paper, we introduce a random elimination strategy from fully annotated joints and propose a deep neural network for SMAL parameters regression from the partial joints. Our proposed method can effectively deal with the reconstruction of animals under the scenario of an occluded image. We have conducted extensive experiments and results demonstrate that our 3D animal shape and pose estimation method can yield good performance on occluded images.
This work was supported in part by Shenzhen Fundamental Research Program under Grant JCYJ20180306174459972, Natural Science Foundation of Jiangsu Province under Grant BK20180355 and the Fundamental Research Funds for the Central Universities.
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Xie, Y., Zhao, Y., Jiang, S., Hu, J., Wang, Y. (2021). Occluded Animal Shape and Pose Estimation from a Single Color Image. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_34
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