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publications
Structural Triangulation: A Closed-Form Solution to Constrained 3D Human Pose Estimation
Published in Computer Vision - ECCV 2022, 2022
We propose Structural Triangulation, a closed-form solution for optimal 3D human pose considering multi-view 2D pose estimations, calibrated camera parameters, and bone lengths. To start with, we focus on embedding structural constraints of human body in the process of 2D-to-3D inference using triangulation. Assume bone lengths are known in prior, then the inference process is formulated as a constrained optimization problem. By proper approximation, the closed-form solution to this problem is achieved. Further, we generalize our method with Step Constraint Algorithm to help converge when large error occurs in 2D estimations. In experiment, public datasets (Human3.6M and Total Capture) and synthesized data are used for evaluation. Our method achieves state-of-the-art results on Human3.6M Dataset when bone lengths are known and competitive results when they are not. The generality and efficiency of our method are also demonstrated.
Recommended citation: Chen, Z., Zhao, X., Wan, X. (2022). Structural Triangulation: A Closed-Form Solution to Constrained 3D Human Pose Estimation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13665. Springer, Cham. https://doi.org/10.1007/978-3-031-20065-6_40
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Joint-Limb Compound Triangulation With Co-Fixing for Stereoscopic Human Pose Estimation
Published in IEEE Transactions on Multimedia, 2024
As a special subset of multi-view settings for 3D human pose estimation, stereoscopic settings show promising applications in practice since they are not ill-posed but could be as mobile as monocular ones. However, when there are only two views, the problems of occlusions and “double counting” (ambiguity between symmetric joints) pose greater challenges that are not addressed by previous approaches. On this concern, we propose a novel framework to detect limb orientations in field form and incorporate them explicitly with joint features. Two modules are proposed to realize the fusion. At 3D level, we design compound triangulation as an explicit module that produces the optimal pose using 2D joint locations and limb orientations. The module is derived from reformulating triangulation in 3D space, and expanding it with the optimization of limb orientations. At 2D level, we propose a parameter-free module named co-fixing to enable joint and limb features to fix each other to alleviate the impact of “double counting”. Features from both parts are first used to infer each other via simple convolutions and then fixed by the inferred ones respectively. We test our method on two public benchmarks, Human3.6 M and Total Capture, and our method achieves state-of-the-art performance on stereoscopic settings and comparable results on common 4-view benchmarks.
Recommended citation: Z. Chen, X. Wan, Y. Bao and X. Zhao, "Joint-Limb Compound Triangulation With Co-Fixing for Stereoscopic Human Pose Estimation," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2024.3410514. keywords: {Three-dimensional displays;Estimation;Pose estimation;Compounds;Stereo image processing;Feature extraction;Vectors;human pose estimation;triangulation;machine learning},
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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