Sandra Aigner

Sandra Aigner, Marco Körner

We introduce a new encoder-decoder GAN model, FutureGAN, that predicts future frames of a video sequence conditioned on a sequence of past frames. During training, the networks solely receive the raw pixel values as an input, without relying on additional constraints or dataset specific conditions. To capture both the spatial and temporal components of a video sequence, spatio-temporal 3d convolutions are used in all encoder and decoder modules. Further, we utilize concepts of the existing progressively growing GAN (PGGAN) that achieves high-quality results on generating high-resolution single images. The FutureGAN model extends this concept to the complex task of video prediction. We conducted experiments on three different datasets, MovingMNIST, KTH Action, and Cityscapes. Our results show that the model learned representations to transform the information of an input sequence into a plausible future sequence effectively for all three datasets. The main advantage of the FutureGAN framework is that it is applicable to various different datasets without additional changes, whilst achieving stable results that are competitive to the state-of-the-art in video prediction. Our code is available at https://github.com/TUM-LMF/FutureGAN.

Peter König, Sandra Aigner, Marco Körner

We present a new two-stage pipeline for predicting frames of traffic scenes where relevant objects can still reliably be detected. Using a recent video prediction network, we first generate a sequence of future frames based on past frames. A second network then enhances these frames in order to make them appear more realistic. This ensures the quality of the predicted frames to be sufficient to enable accurate detection of objects, which is especially important for autonomously driving cars. To verify this two-stage approach, we conducted experiments on the Cityscapes dataset. For enhancing, we trained two image-to-image translation methods based on generative adversarial networks, one for blind motion deblurring and one for image super-resolution. All resulting predictions were quantitatively evaluated using both traditional metrics and a state-of-the-art object detection network showing that the enhanced frames appear qualitatively improved. While the traditional image comparison metrics, i.e., MSE, PSNR, and SSIM, failed to confirm this visual impression, the object detection evaluation resembles it well. The best performing prediction-enhancement pipeline is able to increase the average precision values for detecting cars by about 9% for each prediction step, compared to the non-enhanced predictions.