Geometry-Free View Synthesis: Transformers and no 3D Priors
Geometry-Free View Synthesis: Transformers and no 3D Priors
Robin Rombach*,
Patrick Esser*,
Bjรถrn Ommer
* equal contribution
Interactive Scene Exploration Results
RealEstate10K:
Videos: short (2min) / long (12min)
ACID:
Videos: short (2min) / long (9min)
Demo
For a quickstart, you can try the Colab demo, but for a smoother experience we recommend installing the local demo as described below.
Installation
The demo requires building a PyTorch extension. If you have a sane development environment with PyTorch, g++ and nvcc, you can simply
pip install git+https://github.com/CompVis/geometry-free-view-synthesis#egg=geometry-free-view-synthesis
If you run into problems and have a GPU with compute capability below 8, you can also use the provided conda environment:
git clone https://github.com/CompVis/geometry-free-view-synthesis
conda env create -f geometry-free-view-synthesis/environment.yaml
conda activate geofree
pip install geometry-free-view-synthesis/
Running
After installation, running
braindance.py
will start the demo on a sample scene.
Explore the scene interactively using the WASD keys to move and arrow keys to
look around. Once positioned, hit the space bar to render the novel view with
GeoGPT.
You can move again with WASD keys. Mouse control can be activated with the m
key. Run braindance.py <folder to select image from/path to image> to run the
demo on your own images. By default, it uses the re-impl-nodepth (trained on
RealEstate without explicit transformation and no depth input) which can be
changed with the --model flag. The corresponding checkpoints will be
downloaded the first time they are required. Specify an output path using
--video path/to/vid.mp4 to record a video.
> braindance.py -h
usage: braindance.py [-h] [--model {re_impl_nodepth,re_impl_depth,ac_impl_nodepth,ac_impl_depth}] [--video [VIDEO]] [path]
What's up, BD-maniacs?
key(s) action
=====================================
wasd move around
arrows look around
m enable looking with mouse
space render with transformer
q quit
positional arguments:
path path to image or directory from which to select image. Default example is used if not specified.
optional arguments:
-h, --help show this help message and exit
--model {re_impl_nodepth,re_impl_depth,ac_impl_nodepth,ac_impl_depth}
pretrained model to use.
--video [VIDEO] path to write video recording to. (no recording if unspecified).
Training
Data Preparation
We support training on RealEstate10K
and ACID. Both come in the same format as
described here and the
preparation is the same for both of them. You will need to have
colmap installed and available on your
$PATH.
We assume that you have extracted the .txt files of the dataset you want to
prepare into $TXT_ROOT, e.g. for RealEstate:
> tree $TXT_ROOT
โโโ test
โย ย โโโ 000c3ab189999a83.txt
โย ย โโโ ...
โย ย โโโ fff9864727c42c80.txt
โโโ train
โโโ 0000cc6d8b108390.txt
โโโ ...
โโโ ffffe622a4de5489.txt
and that you have downloaded the frames (we downloaded them in resolution 640 x 360) into $IMG_ROOT, e.g. for RealEstate:
> tree $IMG_ROOT
โโโ test
โย ย โโโ 000c3ab189999a83
โย ย โย ย โโโ 45979267.png
โย ย โย ย โโโ ...
โย ย โย ย โโโ 55255200.png
โย ย โโโ ...
โย ย โโโ 0017ce4c6a39d122
โย ย โย ย โโโ 40874000.png
โย ย โย ย โโโ ...
โย ย โย ย โโโ 48482000.png
โโโ train
โย ย โโโ ...
To prepare the $SPLIT split of the dataset ($SPLIT being one of train,
test for RealEstate and train, test, validation for ACID) in
$SPA_ROOT, run the following within the scripts directory:
python sparse_from_realestate_format.py --txt_src ${TXT_ROOT}/${SPLIT} --img_src ${IMG_ROOT}/${SPLIT} --spa_dst ${SPA_ROOT}/${SPLIT}
You can also simply set TXT_ROOT, IMG_ROOT and SPA_ROOT as environment
variables and run ./sparsify_realestate.sh or ./sparsify_acid.sh. Take a
look into the sources to run with multiple workers in parallel.
Finally, symlink $SPA_ROOT to data/realestate_sparse/data/acid_sparse.
First Stage Models
As described in our paper, we train the transformer models in a compressed, discrete latent space of pretrained VQGANs. These pretrained models can be conveniently downloaded by running
python scripts/download_vqmodels.py
which will also create symlinks ensuring that the paths specified in the training configs (see configs/*) exist.
In case some of the models have already been downloaded, the script will only create the symlinks.
For training custom first stage models, we refer to the taming transformers repository.
Running the Training
After both the preparation of the data and the first stage models are done, the experiments on ACID and RealEstate10K as described in our paper can be reproduced by running
python geofree/main.py --base configs/<dataset>/<dataset>_13x23_<experiment>.yaml -t --gpus 0,
where <dataset> is one of realestate/acid and <experiment> is one of
expl_img/expl_feat/expl_emb/impl_catdepth/impl_depth/impl_nodepth/hybrid.
These abbreviations correspond to the experiments listed in the following Table (see also Fig.2 in the main paper)
Note that each experiment was conducted on a GPU with 40 GB VRAM.
BibTeX
@misc{rombach2021geometryfree,
title={Geometry-Free View Synthesis: Transformers and no 3D Priors},
author={Robin Rombach and Patrick Esser and Bjรถrn Ommer},
year={2021},
eprint={2104.07652},
archivePrefix={arXiv},
primaryClass={cs.CV}
}