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[bibtex]@InProceedings{Chen_2023_ICCV, author = {Chen, Rongyu and Yang, Linlin and Yao, Angela}, title = {MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape Recovery}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {14840-14849} }
MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape Recovery
Abstract
For monocular RGB-based 3D pose and shape estimation, multiple solutions are often feasible due to factors like occlusion and truncation. This work presents a multi-hypothesis probabilistic framework by optimizing the Kullback-Leibler divergence (KLD) between the data and model distribution. Our formulation reveals a connection between the pose entropy and diversity in the multiple hypotheses that has been neglected by previous works. For a comprehensive evaluation, besides the best hypothesis (BH) metric, we factor in visibility for evaluating diversity. Additionally, our framework is label-friendly, in that it can be learned from only partial 2D keypoints, e.g., those that are visible. Experiments on both ambiguous and real-world benchmarks demonstrate that our method outperforms other state-of-the-art multi-hypothesis methods in a comprehensive evaluation. The project page is at https://gloryyrolg.github.io/MHEntropy.
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