| --- |
| library_name: pytorch |
| tags: |
| - motion-capture |
| - smplh |
| license: other |
| license_name: dirtymocap-noncommercial-research |
| license_link: https://huggingface.co/aLongZJU/DirtyMoCap/blob/main/LICENSE |
| --- |
| # DirtyMoCap |
|
|
| Inference weights for [DirtyMoCap](https://github.com/WangLongZJU/DirtyMoCap). |
|
|
| ## License |
|
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| Both **v1 and v-dataset** are governed by the same pretrained model license below. |
|
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| The provided checkpoints may be used only for **non-commercial scientific research, non-commercial education or non-commercial artistic projects**, under the [DirtyMoCap Pretrained Model License](https://huggingface.co/aLongZJU/DirtyMoCap/blob/main/LICENSE). This condition also applies to use of modified or fine-tuned versions. |
|
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| Training used AMASS and GRAB motion data together with SMPL-H/MANO. The `v-dataset` version additionally used BEDLAM2 training data. Users must comply with the applicable [AMASS](https://amass.is.tue.mpg.de/license.html), [GRAB](https://grab.is.tue.mpg.de/license.html), [BEDLAM2](https://bedlam2.is.tuebingen.mpg.de/license.html) (for `v-dataset`) and [SMPL-H/MANO](https://mano.is.tue.mpg.de/license.html) terms. Body models and other third-party assets must be obtained separately under their own licenses. |
|
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| The original DirtyMoCap source code is licensed separately under Apache 2.0. That source-code license does not cover these checkpoints or third-party resources. |
|
|
| ## v1 checkpoints |
|
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| Use both checkpoints from the same revision with the matching DirtyMoCap inference code and configuration. |
|
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| | File | Component | |
| | --- | --- | |
| | [v1/init.ckpt](https://huggingface.co/aLongZJU/DirtyMoCap/blob/main/v1/init.ckpt) | Init network | |
| | [v1/refine_solver.ckpt](https://huggingface.co/aLongZJU/DirtyMoCap/blob/main/v1/refine_solver.ckpt) | Refine/Solver network | |
| | [v1/SHA256SUMS](https://huggingface.co/aLongZJU/DirtyMoCap/blob/main/v1/SHA256SUMS) | SHA256 checksums | |
| | [v1/LICENSE](https://huggingface.co/aLongZJU/DirtyMoCap/blob/main/v1/LICENSE) | Pretrained checkpoint license | |
|
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| The full pipeline uses first-frame Init, temporal Refine in 16-frame windows and Solver in 32-frame windows with a 4-frame overlap and 10 optimization steps. Refine-only inference is also supported. |
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| The checkpoints contain inference network parameters and buffers. SMPL-H/MANO models and DPoser assets must be prepared separately; they are not included in these checkpoint files. |
|
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| ## v-dataset checkpoints |
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| The `v-dataset` pair and matching `configs/inference_dataset.yaml` provide the pipeline used to generate HKMALA-Motion. It uses first-frame Init, temporal Refine and the full Solver; `betas_optimize_windows` is 10. The `v1` release remains available with its own configuration. Both `v1` and `v-dataset` can run inference on your own marker sequences; try both with their matching configurations and choose the version that works better for your data. |
|
|
| ```bash |
| python tools/fetch_checkpoints.py --version v-dataset |
| python -m src.infer --config configs/inference_dataset.yaml --input my_markers.npz --output outputs/motion.npz --gpu 0 |
| ``` |
|
|
| See the [version description](https://huggingface.co/aLongZJU/DirtyMoCap/blob/main/v-dataset/README.md) and [inference guide](https://github.com/WangLongZJU/DirtyMoCap/blob/main/Inference.md#hkmala-motion-inference-with-v-dataset). Model weights use the pretrained checkpoint license. HKMALA-Motion numerical recordings have their own dataset license and access requirements. |
|
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| ## Usage |
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| Prepare the environment, body model and checkpoints with [Install.md](https://github.com/WangLongZJU/DirtyMoCap/blob/main/Install.md). Run this command from the DirtyMoCap code repository root: |
|
|
| ```bash |
| python -m src.infer \ |
| --input demo_samples/sample01.npz \ |
| --output outputs/sample01.npz \ |
| --gpu 0 |
| ``` |
|
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| The included marker-only demos are `demo_samples/sample01.npz`, `demo_samples/sample02.npz` and `demo_samples/sample03.npz`. |
|
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| See [Inference.md](https://github.com/WangLongZJU/DirtyMoCap/blob/main/Inference.md), [Visualization.md](https://github.com/WangLongZJU/DirtyMoCap/blob/main/Visualization.md), [Evaluation.md](https://github.com/WangLongZJU/DirtyMoCap/blob/main/Evaluation.md) and [License.md](https://github.com/WangLongZJU/DirtyMoCap/blob/main/License.md) for input/output formats, the viewer, benchmark evaluation and license scope. |
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