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XRMoCap decomposes the framework into several components, based on which optimization-based and learning-based methods are integrated into one framework. Users can easily prototype a customized multi-view mocap pipeline by choosing different components in configs.
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## News
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- 2022-12-21: XRMoCap [v0.7.0](https://github.com/openxrlab/xrmocap/releases/tag/v0.7.0) is released. Major updates include:
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- Add [mview_mperson_end2end_estimator](https://github.com/openxrlab/xrmocap/blob/main/xrmocap/core/estimation/mview_mperson_end2end_estimator.py) for learning-based method
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- Add SMPLX support and allow smpl_data initiation in `mview_sperson_smpl_estimator`
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- Add multiple optimizers, detailed joint weights and priors, grad clipping for better SMPLify results
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- Add [mediapipe_estimator](https://github.com/openxrlab/xrmocap/blob/main/xrmocap/human_perception/keypoints_estimation/mediapipe_estimator.py) for human keypoints2d perception
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- 2022-10-14: XRMoCap [v0.6.0](https://github.com/openxrlab/xrmocap/releases/tag/v0.6.0) is released. Major updates include:
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- Add [4D Association Graph](http://www.liuyebin.com/4dassociation/), the first Python implementation to reproduce this algorithm
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