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.
Here are some generated 2D animation:
| demo1 | demo2 | demo3 |
|---|---|---|
 |
 |
 |
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:
