This code accompanies the following TPAMI 2020 paper:
A. Palazzi, L. Bergamini, S. Calderara and R. Cucchiara, "Warp and Learn: Novel Views Generation for Vehicles and Other Objects," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2020.3030701
available in Open Access here.
If you find the code / data in this repository useful for your research work, please cite the respective publication.
 |
@article{palazzi2020warp,
title={Warp and Learn: Novel Views Generation for Vehicles and Other Objects},
author={Palazzi, Andrea and Bergamini, Luca and Calderara, Simone and Cucchiara, Rita},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2020},
publisher={IEEE},
doi={10.1109/TPAMI.2020.3030701},
ISSN={1939-3539}
}
|
In this work we introduce a new semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image. Differently from parametric (i.e. entirely learning-based) methods, we show how a-priori geometric knowledge about the object and the 3D world can be successfully integrated into a deep learning based image generation framework. As this geometric component is not learnt, we call our approach semi-parametric.
In particular, we exploit man-made object symmetry and piece-wise planarity to integrate rich a-priori visual information into the novel viewpoint synthesis process. An Image Completion Network (ICN) is then trained to generate a realistic image starting from this geometric guidance.
This careful blend between parametric and non-parametric components allows us to i) operate in a real-world scenario, ii) preserve high-frequency visual information such as textures, iii) handle truly arbitrary 3D roto-translations of the input and iv) perform shape transfer to completely different 3D models.
Eventually, we show that our approach can be easily complemented with synthetic data and extended to other rigid objects with completely different topology, even in presence of concave structures and holes (e.g. chairs). A comprehensive experimental analysis against state-of-the-art competitors shows the efficacy of our method both from a quantitative and a perceptive point of view.
Run the following in a fresh Python 3.6 environment to install all dependencies:
pip install -r requirements.txtCode was tested on Ubuntu linux only (16.04, 17.04).
To run the demo code, please download and unzip all the data from this shared directory in a <data_root> of your choice.
The entry point is run_rotate.py. The script expects as mandatory arguments the object class, pascal dataset, pre-trained weights and 3D models dir.
For the car class it can be run as follows:
python run_rotate.py car <data_root>/pascal_car <data_root>/car_icn.pth <data_root>/car_cads --device cpureplace chair with car to run on the chair class.
You should now see a GUI like the following:
The GUI is composed of two windows: the viewport and the output one.
While the focus is on the viewport, keyboard can be used to move around the object in spherical coordinates. Here the full list of commands is provided. While you move, the output shows both Image Completion Network (ICN) inputs (2.5D sketches, appearance prior) and network prediction. Please refer to Sec.3 of the paper for details.
Notice: it may happen that when starting the program, open3D does not render anything. This is an initialization issue. In case this happens, just focus on the viewport and press spacebar a couple of times until you see both windows rendered properly.
Due to its semi-parametric nature, our method can handle extreme viewpoint changes.
| Manipulating radius | Manipulation elevation | Arbitrary rototranslation |
|---|---|---|
 |
 |
 |
| Chairs backflip |
|---|
 |
Datasets (link for download)
We release two datasets of 3D models (cars, chairs) with annotated 3D keypoints.
Currently, there are 59 annotated models for car and 73 for chair.
3D models come from ShapeNet and have been converted in .ply format (with colors).
Each example of the datasets is composed of the following components:
- One
.plyfile containing the 3D model mesh and colors. - One
.yamlfile containing the 3D keypoints annotation - One
.jpgimage of the model thumbnail.
Annotated keypoints are the ones in Pascal3D+: 12 for cars and 10 for chairs.
Car keypoints: front wheel, back wheel, upper windshield, upper rearwindow, front light, back trunk (2x, left and right).
Chair keypoints: back upper, seat upper, seat lower, leg upper, leg lower (2x, left and right).
We believe that research should be as open as possible and we are happy if these datasets can be helpful for your research too. If you use these data, please cite our research work.
Since 3D models come from ShapeNet database, if you use this dataset you agree to ShapeNet terms of use.