Practical overview
When it fits
Use Mine when you want a local research-code workflow for single-image novel-view rendering or the documented LLFF training setup, rather than a hosted image-generation service.
Who it is for
- Computer-vision researchers investigating novel-view synthesis.
- Developers exploring single-image scene reconstruction or image-to-video rendering.
- Researchers who want to train or evaluate the method using the documented LLFF workflow.
Documented uses
- Render novel viewpoints from a single input image using a released checkpoint.
- Train the method on the LLFF dataset using the provided environment and training script.
- Inspect the method's released pretrained models for RealEstate10K, KITTI and Flowers.
Requirements & limits
The repository is public and identifies the project license as MIT.
- The released checkpoints are listed for RealEstate10K, KITTI and Flowers, with different input resolutions.
- The documented training instructions cover LLFF; other dataset preprocessing and training flows are not included in the README excerpt.
- The training example specifies two GPUs.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Render novel views with a pretrained checkpoint
Use a released checkpoint and the visualization script to generate an image-to-video rendering from an input image.
Before you start
- A downloaded pretrained checkpoint and its hyperparameter YAML file in the same directory.
- An input image and a Python environment for the repository.
Steps
Download a checkpoint from one of the listed RealEstate10K, KITTI or Flowers model entries.
Place the checkpoint and its hyperparameter YAML file together.
Run visualizations/image_to_video.py with the checkpoint path, GPU selection, input image path and output directory.
Expected outcome
The script is configured to render novel scene views from the input image and save output to the specified directory.
Guide limitations
- The listed checkpoint input resolutions vary by dataset and model entry.
- The README does not specify further image-format or hardware requirements.
Set up the documented LLFF training workflow
Prepare the Conda environment and LLFF data, then start training using the repository's example command.
Before you start
- Conda.
- The pre-downsampled LLFF dataset downloaded from the linked Google Drive file.
- The dataset unzipped in the project root.
Steps
Create the environment with conda env create -f environment.yml.
Activate it with conda activate MINE.
Run the documented start_training.sh command configured for LLFF.
Expected outcome
The example workflow directs TensorBoard logs and checkpoints to a subdirectory formed from WORKSPACE and VERSION.
Guide limitations
- The README's example command specifies two GPUs.
- The documented training flow is for LLFF; the README says preprocessing code and training flows for other experimented datasets would be released later.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- vincentfung13/MINE · Official homepage title · retained in its source language