# RT-MPINet #### Real-Time View Synthesis with Multiplane Image Network using Multimodal Supervision (RT-MPINet) We present a real-time multiplane image (MPI) network. Unlike existing MPI based approaches that often rely on a separate depth estimation network to guide the network for estimating MPI parameters, our method directly predicts these parameters from a single RGB image. To guide the network we present a multimodal training strategy utilizing joint supervision from view synthesis and depth estimation losses. More details can be found in the paper. **Please head to the [Project Page](https://realistic3d-miun.github.io/Research/RT_MPINet/index.html) to see supplementary materials and Full Code** ## Acknowledgements - We thank the authors of [AdaMPI](https://github.com/yxuhan/AdaMPI) for their implementation of the homography renderer which has been used in this codebase under `./utils` directory - We tank the author of [Deepview renderer](https://github.com/Findeton/deepview) template, which was used in our project page. ## Citation If you use our work please use following citation: ``` @inproceedings{gond2025rtmpi, title={Real-Time View Synthesis with Multiplane Image Network using Multimodal Supervision}, author={Gond, Manu and Shamshirgarha, Mohammadreza and Zerman, Emin and Knorr, Sebastian and Sj{\"o}str{\"o}m, M{\aa}rten}, booktitle={2025 IEEE 27th International Workshop on Multimedia Signal Processing (MMSP)}, pages={}, year={2025}, organization={IEEE} } ```