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Anysplat

See the Github repository: regarding installation instructions.

TipoOtherVersãoRevision d2e8c34Bnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
2,74 GB
Versão
vRevision d2e8c34
Atualizado
17 de out. de 2026
Downloads
7,5 mil
Fonte
Hugging Face

Descrição da fonte

AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views

Project Website Paper GitHub Repo Hugging Face Model

Quick Start

See the Github repository: https://github.com/OpenRobotLab/AnySplat regarding installation instructions.

The model can then be used as follows:

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 licensed under the MIT License.

Fontes

1 fonte
Hugging FacePrimárioVersão d2e8c343672646041ad4ea518184968f94362f01
huggingface.co/lhjiang/anysplat/tree/d2e8c343672646041ad4ea518184968f94362f01

Verificado 17 de ago.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 2,74 GB · SHA-256 1c4de2ba5a29…0c7c · Hugging Face

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Trechos de fonte

2 trechos

--- license: mit pipeline_tag: image-to-3d --- # AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views [![Project Website](https://img.shields.io/badge/AnySplat-Website-4CAF50?logo=googlechrome&logoColor=white)](https://city-super.github.io/anysplat/) [![Paper](https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=b31b1b)](https://arxiv.org/pdf/2505.23716) [![GitHub Repo](https://img.shields.io/badge/GitHub-Code-FFD700?logo=github)](https://github.com/OpenRobotLab/AnySplat) [![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue)](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...

Source context: 7509 downloads · 14 likes · Pipeline image-to-3d · Repo lhjiang/anysplat