- Age progression transformation install#
- Age progression transformation full#
- Age progression transformation code#
- Age progression transformation download#
Age progression transformation install#
Pip install -r requirements.txt Quick Demo If any of these packages are not installed on your computer, you can install them using the supplied requirements.txt file: The following python packages should also be installed:
Age progression transformation code#
We tested our code on PyTorch 1.4.0 and torchvision 0.5.0, but the code should run on any PyTorch version above 1.0.0, and any torchvision version above 0.4.0. This code requires PyTorch and torchvision to be installed, please go to for installation info. You must have a GPU with CUDA support in order to run the code. If you spot any bias in the results, please reach out to help future research! Pre-Requisits Further work is required to make sure future algorithms will be able to simulate aging for the entire gender spectrum.ĭespite these measures, the network might still introduce other biases that we did not consider when designing the algorithm. We acknowledge that this design choice restricts our algorithm from simulating the aging process of people whose gender is non-binary. The decision of which model to apply is left for the user. producing male facial features for females or vice versa, we have trained two separate models, one for males and one for females. To prevent introducing these biases in the output, e.g.
Age progression transformation download#
Age progression transformation full#
Our framework can predict a full head portrait for ages 0–70 from a single photo, modifying both texture and shape of the head. Fixed age classes are used as anchors to approximate continuous age transformation. The network is trained on the FFHQ dataset, which we labeled for ages, gender, and semantic segmentation. We propose a novel multi-domain image-to-image generative adversarial network architecture, whose learned latent space models a continuous bi-directional aging process. This limits the applicability of previous methods to aging of adults to slightly older adults, and application of those methods to photos of children does not produce quality results. Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process. We address the problem of single photo age progression and regression-the prediction of how a person might look in the future, or how they looked in the past.