Decomposing Motion and Content for Natural Video Sequence Prediction

Introduction

Understanding videos has been one of the most important tasks in the field of computer vision. Compared to still images, the temporal component of videos provides much richer descriptions of the visual world, such as interaction between objects, human activities, and so on. Amongst the various tasks applicable on videos, the task of anticipating the future has recently received increased attention in the research community. Most prior works in this direction focus on predicting high-level semantics in a video such as action (Vondrick et al., 2015; Ryoo, 2011; Lan et al., 2014), event (Yuen and Torralba, 2010; Hoai and Torre, 2013) and motion (Pintea et al., 2014; Walker et al., 2014; Pickup et al., 2014; Walker et al., 2016). Forecasting semantics provides information about what will happen in a video, and is essential to automate decision making. However, the predicted semantics are often specific to a particular task and provide only a partial description of the future. Also, training such models often requires heavily labeled training data which leads to tremendous annotation costs especially with videos.

In this work, we aim to address the problem of prediction of future frames in natural video sequences. Pixel-level predictions provide dense and direct description of the visual world, and existing video recognition models can be adopted on top of the predicted frames to infer various semantics of the future. Spatio-temporal correlations in videos provide a self-supervision for frame prediction, which enables purely unsupervised training of a model by observing raw video frames. Unfortunately, estimating frames is an extremely challenging task; not only because of the inherent uncertainty of the future, but also various factors of variation in videos leading to complicated dynamics in raw pixel values. There have been a number of recent attempts on frame prediction (Srivastava et al., 2015; Mathieu et al., 2015; Oh et al., 2015; Goroshin et al., 2015; Lotter et al., 2015; Ranzato et al., 2014), which use a single encoder that needs to reason about all the different variations occurring in videos in order to make predictions of the future, or require extra information like foreground-background segmentation masks and static background (Vondrick et al., 2016).

We propose a Motion-Content Network (MCnet) for robust future frame prediction. Our intuition is to split the inputs for video prediction into two easily identifiable groups, motion and content, and independently capture each information stream with separate encoder pathways. In this architecture, the motion pathway encodes the local dynamics of spatial regions, while the content pathway encodes the spatial layout of the salient parts of an image. The prediction of the future frame is then achieved by transforming the content of the last observed frame given the identified dynamics up to the last observation. Somewhat surprisingly, we show that such a network is end-to-end trainable without individual path way supervision. Specifically, we show that an asymmetric architecture for the two pathways enables such decompositions without explicit supervision. The contributions of this paper are summarized below:

We propose MCnet for the task of frame prediction, which separates the information streams (motion and content) into different encoder pathways.

The proposed network is end-to-end trainable and naturally learns to decompose motion and content without separate training, and reduces the task of frame prediction to transforming the last observed frame into the next by the observed motion.

We evaluate the proposed model on challenging real-world video datasets, and show that it outperforms previous approaches on frame prediction.

The rest of the paper is organized as follows. We briefly review related work in Section 2, and introduce an overview of the proposed algorithm in Section 3. The detailed configuration of the proposed network is described in Section 4. Section 5 describes training and inference procedure. Section 6 illustrates implementation details and experimental results on challenging benchmarks.

Related work

The problem of visual future prediction has received growing interests in the computer vision community. It has led to various tasks depending on the objective of future prediction, such as human activity (Vondrick et al., 2015; Ryoo, 2011; Lan et al., 2014), event (Yuen and Torralba, 2010; Hoai and Torre, 2013) and geometric path (Walker et al., 2014). Although previous work achieved reasonable success in specific tasks, they are often limited to estimating predefined semantics, and require fully-labeled training data. To alleviate this issue, approaches predicting representation of the future beyond semantic labels have been proposed. Walker et al. (2014) proposed a data-driven approach to predict the motion of a moving object, and coarse hallucination of the predicted motion. Vondrick et al. (2015) proposed a deep regression network to predict feature representations of the future frames. These approaches are supervised and provide coarse predictions of how the future will look like. Our work also focuses on unsupervised learning for prediction of the future, but to a more direct visual prediction task: frame prediction.

Compared to predicting semantics, pixel-level prediction has been less investigated due to the difficulties in modeling evolution of raw pixels over time. Fortunately, recent advances in deep learning provide a powerful tool for sequence modeling, and enable the creation of novel architectures for modeling complex sequential data. Ranzato et al. (2014) applied a recurrent neural network developed for language modeling to frame prediction by posing the task as classification of each image region to one of quantized patch dictionaries. Srivastava et al. (2015) applied a sequence-to-sequence model to video prediction, and showed that Long Short-Term Memory (LSTM) is able to capture pixel dynamics. Oh et al. (2015) proposed an action-conditional encoder-decoder network to predict future frames in Atari games. In addition to the different choices of architecture, some other works addressed the importance of selecting right objective function: Lotter et al. (2015) used adversarial loss with combined CNN and LSTM architectures, and Mathieu et al. (2015) employed similar adversarial loss with additional regularization using a multi-scale encoder-decoder network. Finn et al. (2016) constructed a network that predicts transformations on the input pixels for next frame prediction. Patraucean et al. (2015) proposed a network that by explicitly predicting optical flow features is able to predict the next frame in a video. Vondrick et al. (2016) proposed a generative adversarial network for video which, by generating a background-foreground mask, is able to generate realistic-looking video sequences. However, none of the previously mentioned approaches exploit spatial and temporal information separately in an unsupervised fashion. In terms of the way data is observed, the closest work to ours is Xue et al. (2016). The differences are (1) Our model is deterministic and theirs is probabilistic, (2) our motion encoder is based on convolutional LSTM (Shi et al., 2015) which is a more natural module to model long-term dynamics, (3) our content encoder observes a single scale input and theirs observes many scales, and (4) we directly generate image pixels values, which is a more complicated task. We aim to exploit the existing spatio-temporal correlations in videos by decomposing the motion and content in our network architecture.

To the best of our knowledge, the idea of separating motion and content has not been investigated in the task of unsupervised deterministic frame prediction. The proposed architecture shares similarities to the two-stream CNN (Simonyan and Zisserman, 2014), which is designed for action recognition to jointly exploit the information from frames and their temporal dynamics. However, in contrast to their network we aim to learn features for temporal dynamics directly from the raw pixels, and we use the identified features from the motion in combination with spatial features to make pixel-level predictions of the future.

Algorithm Overview

At the $t$-th time step, our network observes a history of previous consecutive frames up to frame $t$, and generates the prediction of the next frame $\hat{{\mathbf{x}}}_{t+1}$ as follows:

Motion Encoder recurrently takes an image difference input between frame ${\mathbf{x}}_{t}$ and ${\mathbf{x}}_{t-1}$ starting from $t=2$, and produces the hidden representation ${\mathbf{d}}_{t}$ encoding the temporal dynamics of the scene components (Section 4.1).

Content Encoder takes the last observed frame ${\mathbf{x}}_{t}$ as an input, and outputs the hidden representation ${\mathbf{s}}_{t}$ that encodes the spatial layout of the scene (Section 4.2).

Multi-Scale Motion-Content Residual takes the computed features, from both the motion and content encoders, at every scale right before pooling and computes residuals ${\mathbf{r}}_{t}$ (He et al., 2015) to aid the information loss caused by pooling in the encoding phase (Section 4.3).

Combination Layers and Decoder takes the outputs from both encoder pathways and residual connections, ${\mathbf{d}}_{t}$, ${\mathbf{s}}_{t}$, and ${\mathbf{r}}_{t}$, and combines them to produce a pixel-level prediction of the next frame $\hat{{\mathbf{x}}}_{t+1}$ (Section 4.4).

The overall architecture of the proposed algorithm is described in Figure 1. The prediction of multiple frames, $\hat{{\mathbf{x}}}_{t+1:t+T}$, can be achieved by recursively performing the above procedures over $T$ time steps (Section 5). Each component in the proposed architecture is described in the following section.

Architecture

This section describes the detailed configuration of the proposed architecture, including the two encoder pathways, multi-scale residual connections, combination layers, and decoder.

The motion encoder captures the temporal dynamics of the scene’s components by recurrently observing subsequent difference images computed from ${\mathbf{x}}_{t-1}$ and ${\mathbf{x}}_{t}$, and outputs motion features by

2 Content Encoder

The content encoder extracts important spatial features from a single frame, such as the spatial layout of the scene and salient objects in a video. Specifically, it takes the last observed frame ${\mathbf{x}}_{t}$ as an input, and produces content features by

It is important to note that our model employs an asymmetric architecture for the motion and content encoder. The content encoder takes the last observed frame, which keeps the most critical clue to reconstruct spatial layout of near future, but has no information about dynamics. On the other hand, the motion encoder takes a history of previous image differences, which are less informative about the future spatial layout compared to the last observed frame, yet contain important spatio-temporal variations occurring over time. This asymmetric architecture encourages encoders to exploit each of two pieces of critical information to predict the future content and motion individually, and enables the model to learn motion and content decomposition naturally without any supervision.

3 Multi-scale Motion-Content Residual

To prevent information loss after the pooling operations in our motion and content encoders, we use residual connections (He et al., 2015). The residual connections in our network communicate motion-content features at every scale into the decoder layers after unpooling operations. The residual feature at layer $l$ is computed by

where ${\mathbf{r}}_{t}^{l}$ is the residual output at layer $l$, $\left[{\mathbf{s}}_{t}^{l},{\mathbf{d}}_{t}^{l}\right]$ is the concatenation of the motion and content features along the depth dimension at layer $l$ of their respective encoders, $f^{\text{res}}\left(.\right)^{l}$ the residual function at layer $l$ implemented as consecutive convolution layers and rectification with a final linear layer.

4 Combination Layers and Decoder

where ${\mathbf{r}}_{t}$ is a list containing the residual connections from every layer of the motion and content encoders before pooling sent to every layer of the decoder after unpooling. We employ the deconvolution network (Zeiler et al., 2011) for our decoder network $g^{\text{dec}}$, which is composed of multiple successive operations of deconvolution, rectification and unpooling with the addition of the motion-content residual connections after each unpooling operation. The output layer is passed through a $\tanh\left(.\right)$ activation function. Unpooling with fixed switches are used to upsample the intermediate activation maps.

Inference and Training

Section 4 describes the procedures for single frame prediction, while this section presents the extension of our algorithm for the prediction of multiple time steps.

Given an input video, our network observes the first $n$ frames as image difference between frame ${\mathbf{x}}_{t}$ and ${\mathbf{x}}_{t-1}$, starting from $t=2$ up to $t=n$, to encode initial temporal dynamics through the motion encoder. The last frame ${\mathbf{x}}_{n}$ is given to the content encoder to be transformed into the first prediction $\hat{{\mathbf{x}}}_{t+1}$ by the identified motion features.

For each time step $t\in\left[n+1,n+T\right]$, where $T$ is the desired number of prediction steps, our network takes the difference image between the first prediction $\hat{{\mathbf{x}}}_{t+1}$ and the previous image ${\mathbf{x}}_{t}$, and the first prediction $\hat{{\mathbf{x}}}_{t+1}$ itself to predict the next frame $\hat{{\mathbf{x}}}_{t+2}$, and so forth.

2 Training Objective

To train our network, we use an objective function composed of different sub-losses similar to Mathieu et al. (2015). Given the training data $D=\{{\mathbf{x}}^{(i)}_{1,...,T}\}_{i=1}^{N}$, our model is trained to minimize the prediction loss by

where $\alpha$ and $\beta$ are hyper-parameters that control the effect of each sub-loss during optimization. $\mathcal{L}_{\text{img}}$ is the loss in image space from Mathieu et al. (2015) defined by

Here, ${\mathbf{x}}_{t+k}$ and $\hat{{\mathbf{x}}}_{t+k}$ are the target and predicted frames, respectively, and $p$ and $\lambda$ are hyper-parameters for $\mathcal{L}_{p}$ and $\mathcal{L}_{gdl}$, respectively. Intuitively, $\mathcal{L}_{p}$ guides our network to match the average pixel values directly, while $\mathcal{L}_{gdl}$ guides our network to match the gradients of such pixel values. Overall, $\mathcal{L}_{\text{img}}$ guides our network to learn parameters towards generating the correct average sequence given the input. Training to generate average sequences, however, results in somewhat blurry generations which is the reason we use an additional sub-loss. $\mathcal{L}_{\text{GAN}}$ is the generator loss in adversarial training to allow our model to predict realistic looking frames and it is defined by

where ${\mathbf{x}}_{1:t}$ is the concatenation of the input images, ${\mathbf{x}}_{t+1:t+T}$ is the concatenation of the ground-truth future images, $G\left({\mathbf{x}}_{1:t}\right)=\hat{{\mathbf{x}}}_{t+1:t+T}$ is the concatenation of all predicted images along the depth dimension, and $D\left(.\right)$ is the discriminator in adversarial training. The discriminative loss in adversarial training is defined by

$\mathcal{L}_{\text{GAN}}$, in addition to $\mathcal{L}_{\text{img}}$, allows our network to not only generate the target sequence, but also simultaneously enforce realism in the images through visual sharpness that fools the human eye. Note that our model uses its predictions as input for the next time-step during the training, which enables the gradients to flow through time and makes the network robust for error propagation during prediction. For more a detailed description about adversarial training, please refer to Appendix D.

Experiments

In this section, we present experiments using our network for video generation. We first evaluate our network, MCnet, on the KTH (Schuldt et al., 2004) and Weizmann action (Gorelick et al., 2007) datasets, and compare against a baseline convolutional LSTM (ConvLSTM) (Shi et al., 2015). We then proceed to evaluate on the more challenging UCF-101 (Soomro et al., 2012) dataset, in which we compare against the same ConvLSTM baseline and also the current state-of-the-art method by Mathieu et al. (2015). For all our experiments, we use $\alpha=1$, $\lambda=1$, and $p=2$ in the loss functions.

In addition to the results in this section, we also provide more qualitative comparisons in the supplementary material and in the videos on the project website: https://sites.google.com/a/umich.edu/rubenevillegas/iclr2017.

The content encoder of MCnet is built with the same architecture as VGG16 (Simonyan and Zisserman, 2015) up to the third pooling layer. The motion encoder of MCnet is also similar to VGG16 up to the third pooling layer, except that we replace its consecutive $3$x$3$ convolutions with single $5$x$5$, $5$x$5$, and $7$x$7$ convolutions in each layer. The combination layers are composed of $3$ consecutive $3$x$3$ convolutions (256, 128, and 256 channels in each layer). The multi-scale residuals are composed of $2$ consecutive $3$x$3$ convolutions. The decoder is the mirrored architecture of the content encoder where we perform unpooling followed by deconvolution. For the baseline ConvLSTM, we use the same architecture as the motion encoder, residual connections, and decoder, except we increase the number of channels in the encoder in order to have an overall comparable number of parameters with MCnet.

1 KTH and Weizmann action datasets

The KTH human action dataset (Schuldt et al., 2004) contains 6 categories of periodic motions on a simple background: running, jogging, walking, boxing, hand-clapping and hand-waiving. We use person 1-16 for training and 17-25 for testing, and also resize frames to 128x128 pixels. We train our network and baseline by observing 10 frames and predicting 10 frames into the future on the KTH dataset. We set $\beta=0.02$ for training. We also select the walking, running, one-hand waving, and two-hands waving sequences from the Weizmann action dataset (Gorelick et al., 2007) for testing the networks’ generalizability.

For all the experiments, we test the networks on predicting 20 time steps into the future. As for evaluation, we use the same SSIM and PSNR metrics as in Mathieu et al. (2015). The evaluation on KTH was performed on sub-clips within each video in the testset. We sample sub-clips every 3 frames for running and jogging, and sample sub-clips every 20 frames (skipping the frames we have already predicted) for walking, boxing, hand-clapping, and hand-waving. Sub-clips for running, jogging, and walking were manually trimmed to ensure humans are always present in the frames. The evaluation on Weizmann was performed on all sub-clips in the selected sequences.

Results.

Figure 2 summarizes the quantitative comparisons among our MCnet, ConvLSTM baseline and their residual variations. In the KTH test set, our network outperforms the ConvLSTM baseline by a small margin. However, when we test the residual versions of MCnet and ConvLSTM on the dataset (Gorelick et al., 2007) with similar motions, we can see that our network can generalize well to the unseen contents by showing clear improvements, especially in long-term prediction. One reason for this result is that the test and training partitions of the KTH dataset have simple and similar image contents so that ConvLSTM can memorize the average background and human appearance to make reasonable predictions. However, when tested on unseen data, ConvLSTM has to internally take care of both scene dynamics and image contents in a mingled representation, which gives it a hard time for generalization. In contrast, the reason our network outperforms the ConvLSTM baseline on unseen data is that our network focuses on identifying general motion features and applying them to a learned content representation.

Figure 3 presents qualitative results of multi-step prediction by our network and ConvLSTM. As expected, prediction results by our full architecture preserves human shapes more accurately than the baseline. It is worth noticing that our network produces very sharp prediction over long-term time steps; it shows that MCnet is able to capture periodic motion cycles, which reduces the uncertainty of future prediction significantly. More qualitative comparisons are shown in the supplementary material and the project website.

2 UCF-101 dataset

This section presents results on the challenging real-world videos in the UCF-101 (Soomro et al., 2012) dataset. Having collected from YouTube, the dataset contains 101 realistic human actions taken in a wild and exhibits various challenges, such as background clutter, occlusion, and complicated motion. We employed the same network architecture as in the KTH dataset, but resized frames to 240x320 pixels, and trained the network to observe 4 frames and predict a single frame. We set $\beta=0.001$ for training. We also trained our convolutional LSTM baseline in the same way. Following the same protocol as Mathieu et al. (2015) for data pre-processing and evaluation metrics on full images, all networks were trained on Sports-1M (Karpathy et al., 2014) dataset and tested on UCF-101 unless otherwise stated.We use the code and model released by Mathieu et al. (2015) at https://github.com/coupriec/VideoPredictionICLR2016

Results.

Figure 4 shows the quantitative comparisons between our network trained for single-step-prediction and Mathieu et al. (2015). We can clearly see the advantage of our network over the baseline. The separation of motion and contents in two encoder pathways allows our network to identify key motion and content features, which are then fed into the decoder to yield predictions of higher quality compared to the baseline.We were not able to get the model fine-tuned on UCF-101 from the authors so it is not included in Figure 4 In other words, our network only moves what shows motion in the past, and leaves the rest untouched. We also trained a residual version of MCnet on UCF-101, indicated by “MCnet + RES UCF101", to compare how well our model generalizes when trained and tested on the same or different dataset(s). To our surprise, when tested with UCF-101, the MCnet trained on Sports-1M (MCnet + RES) roughly matches the performance of the MCnet trained on UCF-101 (MCnet + RES UCF101), which suggests that our model learns effective representations which can generalize to new datasets. Figure 5 presents qualitative comparisons between frames generated by our network and Mathieu et al. (2015). Since the ConvLSTM and Mathieu et al. (2015) lack explicit motion and content modules, they lose sense of the dynamics in the video and therefore the contents become distorted quickly. More qualitative comparisons are shown in the supplementary material and the project website.

Conclusion

We proposed a motion-content network for pixel-level prediction of future frames in natural video sequences. The proposed model employs two separate encoding pathways, and learns to decompose motion and content without explicit constraints or separate training. Experimental results suggest that separate modeling of motion and content improves the quality of the pixel-level future prediction, and our model overall achieves state-of-the-art performance in predicting future frames in challenging real-world video datasets.

Acknowledgements

This work was supported in part by ONR N00014-13-1-0762, NSF CAREER IIS-1453651, gifts from the Bosch Research and Technology Center, and Sloan Research Fellowship. We also thank NVIDIA for donating K40c and TITAN X GPUs. We thank Ye Liu, Junhyuk Oh, Xinchen Yan, Lajanugen Logeswaran, Yuting Zhang, Sungryull Sohn, Kibok Lee, Rui Zhang, and other collaborators for helpful discussions. R. Villegas was partly supported by the Rackham Merit Fellowship.

References

Appendix

Appendix A Qualitative and quantitative comparison with considerable camera motion and analysis

In this section, we show frame prediction examples in which considerable camera motion occurs. We analyze the effects of camera motion on our best network and the corresponding baselines. First, we analyze qualitative examples on UCF101 (more complicated camera motion) and then on KTH (zoom-in and zoom-out camera effect).

As seen in Figure 9 and Figure 10, our model handles foreground and camera motion for a few steps. We hypothesize that for the first few steps, motion signals from images are clear. However, as images are predicted, motion signals start to deteriorate due to prediction errors. When a considerable amount of camera motion is present in image sequences, the motion signals are very dense. As predictions evolve into the future, our motion encoder has to handle large motion deterioration due to prediction errors, which cause motion signals to get easily confused and lost quickly.

KTH Results.

We were unable to find videos with background motion in the KTH dataset, but we found videos where the camera is zooming in or out for the actions of boxing, handclapping, and handwaving. In Figure 11, we display qualitative for such videos. Our model is able to predict the zoom change in the cameras, while continuing the action motion. In comparison to the performance observed in UCF101, the background does not change much. Thus, the motion signals are well localized in the foreground motion (human), and do not get confused with the background and lost as quickly.

Appendix B Extended quantitative evaluation

In this section, we show additional quantitative comparison with a baseline based on copying the last observed frame through time for KTH and UCF101 datasets. Copying the last observed frame through time ensures perfect background prediction in videos where most of the motion comes from foreground (i.e. person performing an action). However, if such foreground composes a small part of the video, it will result in high prediction quality score regardless of the simple copying action.

In Figure 12 below, we can see the quantitative comparison in the datasets. Copying the last observed frame through time does a reasonable job in both datasets, however, the impact is larger in UCF101. Videos in the KTH dataset comprise simple background with minimal camera motion, which allows our network to easily predict both foreground and background motion, resulting in better image quality scores. However, videos in UCF101 contain more complicated and diverse background which in combination with camera motion present a much greater challenge to video prediction networks. From the qualitative results in Section A and Figures 5, 8, 9, and 10, we can see that our network performs better in videos that contain isolated areas of motion compared to videos with dense motion. A simple copy$/$paste operation of the last observed frame, ensures very high prediction scores in videos where very small motion occur. The considerable score boost by videos with small motion causes the simple copy$/$paste baseline to outperform MCnet in the overall performance on UCF101.

Appendix C UCF101 Motion Disambiguation Experiments

As shown in Figure 13, when we only evaluate on pixels where rough motion is observed, MCnet reflects higher PSNR and SSIM, and clearly outperforms all the baselines in terms of SSIM. The SSIM results show that our network is able to predict a structure (i.e. textures, edges, etc) similar to the grountruth images within the areas of motion. The PSNR results, however, show that our method outperforms the simple copy$/$paste baseline for the first few steps, but then our method performs slightly worse. The discrepancies observed between PSNR and SSIM scores could be due to the fact that some of the predicted images may not reflect the exact pixel values of the groundtruth regardless of the structures being similar. SSIM scores are known to take into consideration features in the image that go beyond directly matching pixel values, reflecting more accurately how humans perceived image quality.

Appendix D Adversarial Training

Mathieu et al. proposed an adversarial training for frame prediction. Inspired by Goodfellow et al. , they proposed a training procedure that involves a generative model $G$ and a discriminative model $D$. The two models compete in a two-player minimax game. The discriminator $D$ is optimized to correctly classify its inputs as either coming from the training data (real frame sequence) or from the generator $G$ (synthetic frame sequence). The generator $G$ is optimized to generate frames that fool the discriminator into believing that they come from the training data. At training time, $D$ takes the concatenation of the input frames that go into $G$ and the images produced by $G$. The adversarial training objective is defined as follows:

where $\left[.,.\right]$ denotes concatenation in the depth dimension, ${\mathbf{x}}_{1:t}$ denotes the input frames to $G$, ${\mathbf{x}}_{t+1:t+T}$ are the target frames, and $G\left({\mathbf{x}}_{1:t}\right)=\hat{{\mathbf{x}}}_{t+1:t+T}$ are the frames predicted by $G$. In practice, we split the minimax objective into two separate, but equivalent, objectives: $\mathcal{L}_{\text{GAN}}$ and $\mathcal{L}_{\text{disc}}$. During optimization, we minimize the adversarial objective alternating between $\mathcal{L}_{\text{GAN}}$ and $\mathcal{L}_{\text{disc}}$. $\mathcal{L}_{\text{GAN}}$ is defined by

where we optimize the parameters of $G$ to minimize $\mathcal{L}_{\text{GAN}}$ while the parameters of $D$ stay untouched. As a result, $G$ is optimized to generate images that make $D$ believe that they come from the training data. Thus, the generated images look sharper, and more realistic. $\mathcal{L}_{\text{disc}}$ is defined by

where we optimize the parameters of $D$ to minimize $\mathcal{L}_{\text{disc}}$, while the parameters of $G$ stay untouched. $D$ tells us whether its input came from the training data or the generator $G$. Alternating between the two objectives, causes $G$ to generate very realistic images, and $D$ not being able to distinguish between generated frames and frames from the training data.