We present a new semi-parametric approach to synthesize novel views of an object from a single monocular image. First, 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) then leverages 2.5D sketches rendered from a 3D CAD as guidance to generate a realistic image. In contrast to concurrent works, we do not rely solely on synthetic data but leverage instead existing datasets for 3D object detection to operate in a real-world scenario. Differently from competitors, our semi-parametric framework allows the handling of a wide range of 3D transformations. Thorough experimental analysis against state-of-the-art baselines shows the efficacy of our method both from a quantitative and a perceptive point of view.

@article{palazzi2019semi, title={Semi-parametric Object Synthesis}, author={Palazzi, Andrea and Bergamini, Luca and Calderara, Simone and Cucchiara, Rita}, journal={arXiv preprint arXiv:1907.10634}, year={2019} }

Run the following in a fresh Python 3.6 environment to install all dependencies:

pip install -r requirements.txt

Code was tested on Ubuntu linux only (16.04, 17.04).

To run our demo code, you need to download the following:

• Pascal3D+ vehicles dataset (.zip file here)
• 3D CADs (.zip file here)
• Pre-trained weights (.pth file here)

Extract both archives in a location of your choice <data_root>; move there the pre-trained weights file too.

The entry point is run_rotate.py. The script expects as mandatory arguments the car dataset, pre-trained weights and CAD dir.

Therefore, it can be run as follows:

python run_rotate.py <data_root>/pascal_car <data_root>/weights.pth <data_root>/cad --device cpu

If everything went well,, you should 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 is much more robust than competitors to extreme viewpoint changes.

Here they are some examples:

Manipulation of radial distance.

Manipulation of elevation.

Arbitrary rototranslation.

Additional examples generated synthetically using our model are shown below.

Each row is generated as follows. Given an image from Pascal3D+, other examples in the same pose are randomly sampled from the dataset. Then, our method is used to transfer the appearance of the latter to the pose of the first. Eventually, generated vehicles are stiched upon the original image. For a seamless collaging, we perform a small Gaussian blur at the mask border.

Percentage of Correct Keypoints (PCK) logged in TensorBoard during training (see Sec. 4.4)