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Works14 from public data
- 34
- Bias-Compensated Integral Regression for Human Pose Estimation29
This paper uncovers an induced bias from integral regression that results from combining the softmax and the expectation operation, and proposes Bias Compensated Integral Regression (BCIR), an integral regression-based framework that compensates for the bias.
- 22
- On the Calibration of Human Pose Estimation13
The proposed Calibrated ConfidenceNet (CCNet) is a light-weight post-hoc addition that improves AP by up to 1.4% on off-the-shelf pose estimation frameworks and facilitates an additional 1.0mm decrease in 3D keypoint error.
- KITRO: Refining Human Mesh by 2D Clues and Kinematic-tree Rotation9
Kinematic-Tree Rotation (KITRO), a novel mesh refinement strategy that explicitly models depth and human kinematic-tree structure, is introduced, which significantly improves 3D joint estimation accuracy and achieves an ideal 2D fit simultaneously.
- Synthetic-to-Real Pose Estimation with Geometric Reconstruction4
This work proposes a reconstruction-based strategy as a complement to pseudo-labelling for synthetic-to-real domain adaptation and provides a novel solution to effectively correct confident yet inaccurate keypoint locations through image reconstruction in domain adaptation.
- Noise-Robust tiny object localization with flows3
This work proposes Tiny Object Localization with Flows (TOLF), a noise-robust localization framework leveraging normalizing flows for flexible error modeling and uncertainty-guided optimization, enabling robust learning under noisy supervision.
- 3
- Learning Unorthogonalized Matrices for Rotation Estimation3
By replacing the orthogonalization incorporated representation with the proposed PRoM in various rotation-related tasks, this work achieves state-of-the-art results on large-scale benchmarks for human pose estimation.
- Humans as Checkerboards: Calibrating Camera Motion Scale for World-Coordinate Human Mesh Recovery2
This paper presents an optimization-free scale calibration framework, Human as Checkerboard (HAC), which innovatively uses the absolute depth of human-scene contact joints as references to calibrate the corresponding relative scene depth from SLAM.
- Semantics-aware Test-time Adaptation for 3D Human Pose Estimation2
This work pioneer the integration of a semantics-aware motion prior for the test-time adaptation of 3D pose estimation and significantly improves state-of-the-art 3D human pose estimation TTA techniques, with more than 12% decrease in PA-MPJPE on 3DPW and 3DHP.
- Online Test-time Adaptation for 3D Human Pose Estimation: A Practical Perspective with Estimated 2D Poses1
This paper addresses adapting models to streaming videos with estimated 2D poses by proposing adaptive aggregation, a two-stage optimization, and local augmentation for handling varying levels of estimated pose error.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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