TriplaneGaussian

--- license: apache-2.0 pipeline_tag: image-to-3d --- # TriplaneGuassian Model Card <div align="center"> [**Project Page**](https://zouzx.github.io/TriplaneGaussian/) **|** [**Paper (ArXiv)**](https://arxiv.org/abs/2312.09147) **|** [**Code**](https://github.com/VAST-AI-Research/TriplaneGaussian) **|** [**Gradio demo**](https://huggingface.co/spaces/VAST-AI/TriplaneGaussian) </div> ## Introduction TGS enables fast reconstruction from single-view image in a few seconds based on a hybrid Triplane-Gaussian 3D representation. ## Examples ### Results on Images Generated by [Midjourney](https://www.midjourney.com/) <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/644dbf6453ad80c6593bf748/BcJp8alZRXAIdPmfbVGdx.qt"></video> ### Results on Captured Real-world Images <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/644dbf6453ad80c6593bf748/bgAxqUQpnisQAmsGZ9Q_0.qt"></video> ## Model Details The model `model_lvis_rel.ckpt` is trained on Objaverse-LVIS dataset, which only includes ~45K synthetic objects. ## Usage You can directly download the model in this repository or employ the model in python script by: ```python from huggingface_hub import hf_hub_download MODEL_CKPT_PATH = hf_hub_download(repo_id="VAST-AI/TriplaneGaussian", filename="model_lvis_rel.ckpt", repo_type="model") ``` More details can be found in our [Github repository](https://github.com/VAST-AI-Research/TriplaneGaussian). ## Citation If you find this work helpful, please consider citing our paper: ```bibtex @article{zou2023triplane, title={Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers}, author={Zou, Zi-Xin and Yu, Zhipeng and Guo, Yuan-Chen and Li, Yangguang and Liang, Ding and Cao, Yan-Pei and Zhang, Song-Hai}, journal={arXiv preprint arXiv:2312.09147}, year={2023} } ```

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