Thin-Plate Spline-based Interpolation for Animation Line Inbetweening
Authors: Tianyi Zhu, Wei Shang, Dongwei Ren
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on benchmark datasets, and our method is compared against state-of-the-art techniques in video interpolation (Huang et al. 2022; Zhang et al. 2023; Li et al. 2023), animation interpolation (Chen and Zwicker 2022), and line inbetweening (Siyao et al. 2023). The evaluation metrics include CD and WCD scores, as well as introducing Earth Mover s Distance (EMD) and user study into consideration. Our approach outperforms existing methods by producing high-quality interpolation results with enhanced fluidity for all three interpolation gaps, i.e., 1, 5, and 9. |
| Researcher Affiliation | Academia | 1Faculty of Computing, Harbin Institute of Technology 2Tianjin Key Lab of Machine Learning, College of Intelligence and Computing, Tianjin University |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical equations, but it does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | Code https://github.com/Tian-one/tps-inbetween |
| Open Datasets | Yes | The training and testing were conducted on Mixiamo Line240 dataset (Siyao et al. 2023), a line art dataset with ground truth geometrization and vertex matching labels. |
| Dataset Splits | No | We set the frame gap N = 5 during training, and tested on the test set with the gaps N = 1, 5, 9 respectively. The paper mentions a 'test set' but does not provide specific percentages or counts for a full train/validation/test split of the Mixiamo Line240 dataset itself. |
| Hardware Specification | Yes | The training and testing were performed on an NVIDIA RTX A6000 GPU. |
| Software Dependencies | No | We implemented our model in PyTorch (Paszke et al. 2019). We apply Glue Stick (Pautrat et al. 2023) as our keypoints matching model. The paper mentions PyTorch and Glue Stick but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We set the frame gap N = 5 during training, and tested on the test set with the gaps N = 1, 5, 9 respectively. Our model was trained at the resolution of 512 x 512, and tested at the original resolution 720 x 720. We employed the Adam (Kingma and Ba 2015) optimizer with β1 = 0.9 and β2 = 0.999 at a learning rate of 1 x 10-4 for 50 epochs. The hyperparameters λlpips, λcnt, λbi and η were set as 5, 5, 1 x 10-3 and 0.9, respectively. |