Awesome

DynaCam

| [Paper] | [Video] | [Project Page] |

DynaCam contains in-the-wild RGB videos captured by dynamic cameras, including annotations:

3D human trajectories in world coordinates

For the details, please refer to our project page.

Download

[Google drive]

[Baidu drive (百度网盘)]

The structure of dataset is supposed to be:

|-- DynaCam
| --|-- video_frames
|   |   |-- panorama_test
|   |   |-- panorama_train
|   |   |-- panorama_val
|   |   |-- translation_test
|   |   |-- translation_train
|   |   |-- translation_val
|   |-- annotations
|   |   |-- *.npz

Visualization

To visualize each video sequences and corresponding annotations, like 3D human trajectory, please download the SMPL_NEUTRAL.pkl and put it into 'assets/' , then run

sh install.sh
# set the path to dynacam_folder in show_examples.py 
python show_examples.py

<p float="center"> <img src="https://github.com/Arthur151/DynaCam/releases/download/predictions/dynacam_vis_examples.gif" width="50%" /> </p> </p> Press `stop` to stop the animation, draw the `slider` to sellect the frame, press `ESC` on your keyboard to go next.

Evaluation

To re-implement all results on DynaCam in our paper, please download predictions, set the path in evaluation.py to ensure the structure like

|-- predictions
| --|-- TRACE
| --|-- GLAMR
| --|-- bev_dpvo

, then run:

sh install.sh
python evaluation.py

Citation

Please cite our paper if you use DynaCam in your research.

@InProceedings{TRACE,
    author = {Sun, Yu and Bao, Qian and Liu, Wu and Mei, Tao and Black, Michael J.},
    title = {{TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments}}, 
    booktitle = {IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)}, 
    month = June, 
    year = {2023}}