<h1 align="center">MVInverse: Feed-forward Multi-view Inverse Rendering in Seconds</h1> <p align="center"> <a href="https://arxiv.org/abs/2512.21003" target="_blank"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b.svg" alt="arXiv"></a> <a href="https://maddog241.github.io/mvinverse-page/" target="_blank"><img src="https://img.shields.io/badge/Project-Page-orange" alt="Project Page"></a> <a href="https://huggingface.co/spaces/maddog241/mvinverse-demo" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-yellow" alt="HF Demo"></a> <a href="https://huggingface.co/maddog241/mvinverse" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue" alt="HF Model"></a> </p> <div align="center"> <a href="https://maddog241.github.io/mvinverse-page/"> <img src="assets/framework.png" width="90%"> </a> <p> <i>MVInverse enables feed-forward, multi-view consistent inverse rendering without per-scene optimization</i> </p> </div>

🔔 Updates

[December 24, 2025] Inference code release.

🌟 Overview

We introduce MVInverse, aiming to address the limitations of existing methods—such as inconsistent results or high computational costs—when reconstructing scene geometry and materials from multiple images. It introduces a feed-forward framework that leverages alternating attention mechanisms to directly and coherently predict holistic scene properties from an image sequence, achieving state-of-the-art performance in multi-view consistency, material and normal estimation quality.

Usage

1. Clone & Install Dependencies

First, clone the repository and install the required packages.

git clone https://github.com/Maddog241/mvinverse.git
cd mvinverse
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu118
pip install opencv-python huggingface_hub==0.35.0

2. Run Inference from Command Line

You can run inference directly using the provided script. It processes a directory of images and generates corresponding material and geometry maps for each input frame.

Run on the example data (replace with the actual path to your model checkpoint)

python inference.py --data_path examples/Courtroom --save_path <your/output/dir>

Run on your own data

python inference.py --data_path <path/to/your/images_dir> --save_path <your/output/dir>

Arguments:

data_path: Path to the input image directory.
ckpt: Path to the model checkpoint file.
save_path: Directory where the output images will be saved.
num_frames: Number of frames to process. Set to -1 to process all images in the directory.
device: Device to run inference on (cuda or cpu).

🙏 Acknowledgements

Our work is built upon these fantastic open-source projects:

Mvinverse

Install / Use

README