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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Official project website for the CVPR 2020 paper (Oral Presentation) "Cascaded deep monocular 3D human pose estimation wth evolutionary training data"
| Date | Stars |
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| 2026-07-24 | 342 |
| 2026-07-25 | 342 |
| 2026-07-28 | 342 |
| 2026-07-30 | 342 |
| 2026-08-06 | 342 |
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[](https://paperswithcode.com/sota/weakly-supervised-3d-human-pose-estimation-on?p=cascaded-deep-monocular-3d-human-pose-1) [](https://paperswithcode.com/sota/monocular-3d-human-pose-estimation-on-human3?p=cascaded-deep-monocular-3d-human-pose-1) [](https://paperswithcode.com/sota/3d-human-pose-estimation-on-human36m?p=cascaded-deep-monocular-3d-human-pose-1) # EvoSkeleton This is the project website containing relevant files for the CVPR 2020 paper "Cascaded deep monocular 3D human pose estimation with evolutionary training data". The usage and instructions are organized into several parts serving distinct purposes. Please visit the corresponding sub-page for details. For Q&A, go to [discussions](https://github.com/Nicholasli1995/EvoSkeleton/discussions). If you believe there is a technical problem, submit to [issues](https://github.com/Nicholasli1995/EvoSkeleton/issues). News: (2021-04-08): Release v-1.0. The support for pre-trained models is strengthened. More details have been added to the supplementary material. ## Cascaded 2D-to-3D Lifting [This sub-page](https://github.com/Nicholasli1995/EvoSkeleton/blob/master/docs/TRAINING.md) details how to train a cascaded model to lift 2D key-points to 3D skeletons on H36M. If you do not want to prepare synthetic data and train the model by yourself, you can access an examplar pre-trained model [here](https://drive.google.com/file/d/158oCTK-9Y8Bl9qxidoHcXfqfeeA7qT93/view?usp=sharing) and follow the instructions in the [document](https://github.com/Nicholasli1995/EvoSkeleton/blob/master/docs/TRAINING.md). This model can be used for in-the-wild inference as well as reproducing the results on MPI-INF-3DHP. The evaluation metric for MPI-INF-3DHP can be accessed [here](https://github.com/chenxuluo/OriNet-demo/tree/master/src/test_util). <p align="center"> <img src="https://github.com/Nicholasli1995/EvoSkeleton/blob/master/imgs/architecture.jpg"/> </p> Performance on H36M ([Link to pre-trained models](https://github.com/Nicholasli1995/EvoSkeleton/blob/master/docs/Zoo.md)) | Protocol \#1| Avg.|Dir. | Disc| Eat| Greet| Phone| Photo | Pose | Purch.| Sit| SitD.| Smoke| Wait| WalkD.| Walk | WalkT.| |-------------------------------------------------------------|------------------|------------------|---------------|------------------|---------------|---------------|------|---------------|------------------|------------------|---------------|---------------|---------------|---------------|---------------|---------------| | [Martinez](https://github.com/una-dinosauria/3d-pose-baseline) et al. (ICCV'17) |62.9| 51.8 | 56.2| 58.1| 59.0 | 69.5 | 78.4| 55.2 | 58.1 | 74.0 | 94.6| 62.3 | 59.1 | 65.1 | 49.5 | 52.4 | | Ours (S15678) |**49.7**|**45.6**|**44.6**|**49.3**|**49.3**|**52.5**|**58.5**|**46.4**|**44.3**|**53.8**|**67.5**|**49.4**|**46.1**|**52.5**|**41.4**|**44.4**| | Protocol \#2| Avg.|Dir. | Disc| Eat| Greet| Phone| Photo | Pose | Purch.| Sit| SitD.| Smoke| Wait| WalkD.| Walk | WalkT.| |-------------------------------------------------------------|------------------|------------------|---------------|------------------|---------------|---------------|------|---------------|------------------|------------------|---------------|---------------|---------------|---------------|---------------|---------------| | [Martinez](https://github.com/una-dinosauria/3d-pose-baseline) et al. (ICCV'17) |47.7| 39.5 | 43.2 | 46.4 | 47.0 | 51.0| 56.0 | 41.4 | 40.6 | 56.5 | 6
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:021c7932a23439fc, topic:synthetic-data, topic:data-augmentation, readme:synthetic data
matched fp:021c7932a23439fc, topic:deep-learning
matched fp:021c7932a23439fc, topic:dataset, desc:training data, readme:training data