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Title: Unsupervised Video-to-Video Translation

Paper content:
Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time.
In this paper, we formulate a new task of unsupervised video-to-video translation, which poses its own unique challenges.
Translating video implies learning not only the appearance of objects and scenes but also realistic motion and transitions between consecutive frames.
We investigate the performance of per-frame video-to-video translation using existing image-to-image translation networks, and propose a spatio-temporal 3D translator as an alternative solution to this problem.
We evaluate our 3D method on multiple synthetic datasets, such as moving colorized digits, as well as the realistic segmentation-to-video GTA dataset and a new CT-to-MRI volumetric images translation dataset.
Our results show that frame-wise translation produces realistic results on a single frame level but underperforms significantly on the scale of the whole video compared to our three-dimensional translation approach, which is better able to learn the complex structure of video and motion and continuity of object appearance.
Recent work on unsupervised image-to-image translation BID10 BID12 has shown astonishing results on tasks like style transfer, aerial photo to map translation, day-to-night photo translation, unsupervised semantic image segmentation and others.
Such methods learn from unpaired examples, avoiding tedious data alignment by humans.
In this paper, we propose a new task of unsupervised video-to-video translation, i.e. learning a mapping from one video domain to another while preserving high-level semantic information of the original video using large numbers of unpaired videos from both domains.
Many computer vision tasks can be formulated as video-to-video translation, e.g., semantic segmentation, video colorization or quality enhancement, or translating between MRI and CT volumetric data (illustrated in FIG0 ).
Moreover, motion-centered tasks such as action recognition and tracking can greatly benefit from the development of robust unsupervised video-to-video translation methods that can be used out-of-the-box for domain adaptation.Since a video can be viewed as a sequence of images, one natural approach is to use an image-toimage translation method on each frame, e.g., applying a state-of-art method such as CycleGAN , CoGAN BID10 or UNIT BID12 .
Unfortunately, these methods cannot preserve continuity and consistency of a video when applied frame-wise.
For example, colorization of an object may have multiple correct solutions for a single input frame, since some objects such as cars can have different colors.
Therefore, there is no guarantee that an object would preserve its color if translation is performed on the frame level frame.In this paper, we propose to translate an entire video as a three-dimensional tensor to preserve its cross-frame consistency and spatio-temporal structure.
We employ multiple datasets and metrics to evaluate the performance of our proposed video-to-video translation model.
Our synthetic datasets include videos of moving digits of different colors and volumetric images of digits imitating medical scans.
We also perform more realistic segmentation-to-RGB and colorization experiments on the GTA dataset BID14 , and propose a new MRI-to-CT dataset for medical volumetric image translation, which to our knowledge is the first open medical dataset for unsupervised volumeto-volume translation.
We propose the task of unsupervised video-to-video translation.
Left: Results of MR-to-CT translation.
Right: moving MNIST digits colorization.
Rows show per-frame CycleGAN (2D) and our spatio-temporal extension (3D).
Since CycleGAN takes into account information only from the current image, it produces reasonable results on the image level but fails to preserve the shape and color of an object throughout the video.
Best viewed in color.Figure 2: Results of GTA video colorization show that per-frame translation of videos does not preserve constant colours of objects within the whole sequence.
We provide more results and videos in the supplementary video: https://bit.ly/2R5aGgo.
Best viewed in color.Our extensive experiments show that the proposed 3D convolutional model provides more accurate and stable video-to-video translation compared to framewise translation with various settings.
We also investigate how the structure of individual batches affects the training of framewise translation models, and find that structure of a batch is very important for stable translation contrary to an established practice of shuffling training data to avoid overfitting in deep models BID3 .To
summarize, we make the following main contributions: 1)
a new unsupervised video-to-video translation task together with both realistic and synthetic proof-of-concept datasets; 2)
a spatiotemporal video translation model based on a 3D convnet that outperforms per-frame methods in Figure 3 : Our model consists of two generator networks (F and G) that learn to translate input volumetric images from one domain to another, and two discriminator networks (D A and D B ) that aim to distinguish between real and fake inputs. Additional
cycle consistency property requires that the result of translation to the other domain and back is equal to the input video, DISPLAYFORM0 all experiments, according to human and automatic metrics, and 3) an additional
analysis of how performance of per-frame methods depends on the structure of training batches.
We proposed a new computer vision task of unsupervised video-to-video translation as well as datasets, metrics and multiple baselines: multiple approaches to framewise translation using imageto-image CycleGAN and its spatio-temporal extension 3D CycleGAN.
The results of exhaustive experiments show that per-frame approaches cannot capture the essential properties of videos, such as global motion patterns and shape and texture consistency of translated objects.
However, contrary to the previous practice, sequential batch selection helps to reduce motion artifacts.

assistant: text: Proposed new task, datasets and baselines; 3D Conv CycleGAN preserves object properties across frames; batch structure in frame-level methods matters.