Fast Sprite Decomposition from Animated Graphics (ECCV2024)

This repository is an official implementation of the paper "Fast Sprite Decomposition from Animated Graphics". Please also refer to the project page, paper, and our Crello Animation dataset.

Setup

We check the reproducibility under this environment.

• Python 3.10
• CUDA 11.3

After constructing Python environment, install this project with the following command:

bash pip install -e fsd

Decomposition

Sample-level

You can decompose a video by running the following command:

bash ./scripts/optimize_sample.sh \ in_dir="./inputs/62a87468079dcd93634632e7" \ out_dir="./_outputs/62a87468079dcd93634632e7" \ engine.n_iters=60000 \ device=cuda

We provide some examples of inputs (video and foreground box) from Crello Animation in ./inputs and corresponding outputs (sprites) in ./outputs. You can specify the input data by changing the in_dir argument.

We manage configurations using hydra and you can override the default configuration (our best) by specifying the arguments. We illustrate some useful arguments to adjust your optimization as follows:

• engine.n_iters: number of iterations for optimization.
• engine.save_interval: iteration interval for saving the optimization progress.
• engine.eval_interval: iteration interval for evaluating the decomposition quality, which is logged in progress.json.
• engine.timelimit_minute: time limit for each optimization in minutes. If the optimization exceeds this limit, it will be terminated even if the number of iterations is not reached.
• engine.resume: if True, resume the optimization from the last checkpoint in out_dir.
• device: device to run the optimization. You can specify cuda or cpu.

Outputs are stored in the following directory structure:

bash {out_dir} ├── sprites # Decomposed sprites │   ├── iter_* │   │   ├── params.json # Parameters including affine matrices and opacity │   │   └── textures # Textures │   │   ├── 0.png │   │   ├── 1.png │   │   └── 2.png │   └── iter_*.png # Output visualization ├── last_checkpoint.pth # Last checkpoint of optimization └── progress.json # Progress log including evaluation results and elapsed time

You can refer to ./inputs/{VIDEO_ID}/{VIDEO_ID}.png and ./outputs/{VIDEO_ID}/animation/iter_*.png for a brief understanding of the decomposition result. Below is an example:

Input:

Output visualization:

The leftmost is the reconstruction result, the next is the background, and the rest are sprites.

on Crello Animation

You can also conduct decomposition and evaluation on Crello Animation by running the following command:

bash ./scripts/optimize.sh \ out_dir="./_outputs/crello-animation/" \ data.split="val" \ device=cuda

data.split can be val or test. You will obtain the same output structure as the sample-level decomposition for all samples in Crello Animation.

Citation

If you find this code useful for your research, please cite our paper.

bibtex @inproceedings{suzuki2024fast, title={Fast Sprite Decomposition from Animated Graphics}, author={Suzuki, Tomoyuki and Kikuchi, Kotaro and Yamaguchi, Kota}, booktitle={ECCV}, year={2024} }

Acknowledgments

Thanks to the following repositories that we referred to in part: Track-Anything and deformable-sprites