Preprocess a video for training using Docker

Our preprocessing pipeline uses COLMAP, Detectron2, mmpose, ROMP, and BoostingMonocularDepth to estimate the 2D or 3D geometry of the scene or the human.

Build docker image:
1. ```
docker build -t neuman .
```
Start the docker container:

To virtualize the GPUs, we need to install nvidia-docker.
```
docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -ti -v /path/to/data_folder:/data -v /path/to/smplx:/neuman/data/smplx --entrypoint bash neuman
```
Notice that we mount two folders to the Docker container: /path/to/data_folder is the path to the video folder on the host system, and /path/to/smplx is the path to smplx folder on the host system(for more details, see Demo section here)
Preprocess inside the container:
1. Generate the shell script:
```
cd /neuman/preprocess;
python gen_run.py --video /data/my_video.mov
```
2. Run the whole preprocess pipeline:
```
bash -i ./run.sh
```
3. The final output will be save at my_video/output.
Visual check:
1. Check SMPL estimates, at overlay/optimized folder, please make sure the human subject has a corresponding SMPL estimates. Example overlay:
1. Check scene and SMPL mesh alignment, run: python visualize_scene.py --scene_dir path/to/output to visualize both, please make sure the camera trajectory looks reasonable, and the feet of SMPL meshes are touching the ground plane. Example scene and SMPL mesh alignment:

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