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Subject-Specific Human Modeling for Human Pose Estimation
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DOI：10.1109/THMS.2022.3195952.png)
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摘要
En 中文
3-D human pose estimation or human tracking has always been the focus of research in the human-computer interaction community. As the calibration step of human pose estimation, subject-specific modeling is crucially important to the subsequent pose estimation process. It not only provides a priori knowledge but also clearly defines the tracking target. This article presents a fully automatic subject modeling framework to reconstruct human pose, shape, as well as the body texture in a challenging optimization scenario. By integrating powerful differentiable rendering into the subject-specific modeling pipeline, the proposed method transforms the texture reconstruction problem into analysis by synthesis minimization and solves it efficiently by a gradient-based method. Furthermore, a novel covariance matrix adaptation annealing algorithm is proposed to attack the high-dimensional multimodal optimization problem in an adaptive manner. The domain knowledge of hierarchical human anatomy is seamlessly injected to the annealing optimization process by using a soft covariance matrix mask. All together contributes to the novel algorithm robust to the temptation of local minima. Experiments on the Human3.6 M dataset and the People-Snapshot dataset demonstrate the competitive results to the state of the art both qualitatively and quantitatively.
Keyword:
Shape
Optimization
Rendering (computer graphics)
Pose estimation
Image reconstruction
Annealing
Perturbation methods
Differentiable rendering
human pose estimation
subject-specific modeling
texture reconstruction
期刊
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4.4
论文数:
1.1K
被引数:
3.5K
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