Official implementation of SIGGRAPH Asia paper -- SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation

Installing Dependencies

Dependencies can be installed using:

pip install -r requirements.txt

Datasets

Image Morph

Given two shapes, we first render multi-view images by:

python render_folder.py

Then use DiffMorpher [1] to generate image interpolation with respect to each views.

Dynamic 3D Gaussian

We convert the image interpolation results to SC-GS [2] inputs by:

python conver_npy_to_dataset.py

Then use SC-GS [2] to obtain a set of dense and noisy point clouds.

Training

python train.py

Testing

python test.py

References

[1]: Zhang, K., Zhou, Y., Xu, X., Dai, B., & Pan, X. (2024). DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[2]: Huang, Y. H., Sun, Y. T., Yang, Z., Lyu, X., Cao, Y. P., & Qi, X. (2024). Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4220-4230).