This is the simplified version of facebookresearch/AnimatedDrawings: Code to accompany "A Method for Animating Children's Drawings of the Human Figure" (github.com). here we can custom your monocular video and drawing as input, framework will automatically generate corresponding animation. All processes are in end-to-end way.
If you want to see more details in initial README.md, clik here
This project has been tested with macOS Ventura 13.2.1 and Ubuntu 18.04. If you're installing on another operating system, you may encounter issues.
We strongly recommend activating a Python virtual environment prior to installing Animated Drawings. Conda's Miniconda is a great choice. Follow these steps to download and install it. Then run the following commands:
# create and activate the virtual environment
conda create --name animated_drawings python=3.8.13
conda activate animated_drawings
# clone AnimatedDrawings and use pip to install
git clone https://github.com/Brian417-cup/AnimatedDrawings
cd AnimatedDrawings
pip install -e .Mac M1/M2 users: if you get architecture errors, make sure your ~/.condarc does not have osx-64, but
only osx-arm64 and noarch in its subdirs listing. You can see that it's going to go sideways as early
as conda create because it will show osx-64 instead of osx-arm64 versions of libraries under "The following NEW
packages will be INSTALLED".
Now, to simplify process, all you need to do is provide a drawing and a single person video to generate an animation in the offline mode and virutal conda environment locally!!
Here are tips:
- Download the corresponding resource file from the provided link, and then put them under corresponding directories respectively:
| resource name | target directory |
|---|---|
| sketch_detector.onnx | examples/offline_res/checkpoint |
| sketch_estimator.onnx | examples/offline_res/checkpoint |
| yolov8 | examples/offline_lib/pose3d/vitpose/checkpoints |
| vitpose-b-coco.onnx | examples/offline_lib/pose3d/vitpose/checkpoints |
| pose3d.onnx | examples/offline_lib/pose3d/checkpoint |
- Use the following command:
cd examples
python offline_demo.py \
--src_sketch <your_custom_drawing_path> \
--src_motion <your_source_video_path_or_bvh_file_path> \
--out_vid <output_video_path>Attention: Currently, the projection way proposed in the thesis is static, if you want to get more interesting projection way, please modify certain retarget config file.
The final result may get this:
