Anysplat

--- license: mit pipeline_tag: image-to-3d --- # AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views [](https://city-super.github.io/anysplat/) [](https://arxiv.org/pdf/2505.23716) [](https://github.com/OpenRobotLab/AnySplat) [](https://huggingface.co/lhjiang/anysplat) ## Quick Start See the Github repository: https://github.com/OpenRobotLab/AnySplat regarding installation instructions. The model can then be used as follows: ```python from pathlib import Path import torch import os import sys sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from src.misc.image_io import save_interpolated_video from src.model.model.anysplat import AnySplat from src.utils.image import process_image # Load the model from Hugging Face model = AnySplat.from_pretrained("anysplat_ckpt_v1") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) model.eval() for param in model.parameters(): param.requires_grad = False # Load and preprocess example images (replace with your own image paths) image_names = ["path/to/imageA.png", "path/to/imageB.png", "path/to/imageC.png"] images = [process_image(image_name) for image_name in image_names] images = torch.stack(images, dim=0).unsqueeze(0).to(device) # [1, K, 3, 448, 448] b, v, _, h, w = images.shape # Run Inference gaussians, pred_context_pose = model.inference((images+1)*0.5) pred_all_extrinsic = pred_context_pose['extrinsic'] pred_all_intrinsic = pred_context_pose['intrinsic'] save_interpolated_video(pred_all_extrinsic, pred_all_intrinsic, b, h, w, gaussians, image_folder, model.decoder) ``` ## Citation ``` @article{jiang2025anysplat, title={AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views}, author={Jiang, Lihan and Mao, Yucheng and Xu, Linning and Lu, Tao and Ren, Kerui and Jin, Yichen and Xu, Xudong and Yu, Mulin and Pang, Jiangmiao and Zhao, Feng and others}, journal={arXiv preprint arXiv:2505.23716}, year={2025} } ``` ## License The code and models are l...

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