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适用人群本论文适用于以下专业读者：计算机视觉和机器学习领域的研究人员和学者。对生成对抗网络（GANs）在视频生成任务上的应用感兴趣的工程师和开发者。探索深度学习在视频处理和动作识别中应用的数据科学家。人工智能领域的学生和教育工作者，特别是那些专注于视频内容生成和分析的。使用场景及目标研究与开发：研究人员可以使用DVD-GAN模型来探索视频生成的新方法，提高视频合成和预测的质量和效率。教育应用：作为教学案例，帮助学生理解GANs在视频处理领域的应用，以及如何评估生成模型的性能。工业应用：在娱乐、虚拟现实、游戏开发等行业中，利用DVD-GAN生成的视频内容创造新的用户体验。数据分析：数据科学家可以使用DVD-GAN来模拟视频数据，用于增强现有数据集，或进行数据增强以改善机器学习模型的训练。技术评估：研究人员和开发人员可以利用论文中提到的评估指标（如IS和FID）来比较不同模型生成的视频质量。论文的目标是通过展示DVD-GAN在复杂视频数据集上的应用，推动视频生成技术的发展，并为未来在更大规模和更复杂数据集上的模型训练和评估提供基准。通过这项研究，作者希望强调在大型和复杂的视频数据集上训练生成模型的重要性，并期待DVD-GAN能成为未来研究的参考点。 ### MoCoGAN: 分解运动与内容以实现视频生成 #### 概述《MoCoGAN: Decomposing Motion and Content for Video Generation》是一篇聚焦于视频生成领域的重要论文。该研究由Sergey Tulyakov（Snap Research）、Ming-Yu Liu、Xiaodong Yang及Jan Kautz（NVIDIA）共同完成。MoCoGAN的主要贡献在于提出了一种能够有效分解视频中的“内容”和“运动”的生成对抗网络框架，从而实现了高质量的视频生成。这一工作不仅对计算机视觉和机器学习领域的研究者具有重要意义，同时也为视频处理和动作识别提供了有力的技术支持。 #### 适用人群与应用场景 **适用人群**： 1. **计算机视觉与机器学习领域的研究者**：MoCoGAN为视频生成的研究提供了新的思路和技术手段，有助于推动相关领域的学术发展。 2. **对生成对抗网络（GANs）感兴趣的工程师与开发者**：该论文深入探讨了GANs在视频生成任务中的应用，对于理解GANs的工作原理及其优化方法具有指导意义。 3. **专注于视频处理和动作识别的数据科学家**：通过MoCoGAN，数据科学家可以更好地理解和模拟视频数据，进而提升机器学习模型的性能。 4. **人工智能领域的学生与教育工作者**：MoCoGAN可以作为教学案例，帮助学生掌握视频生成的基本概念和技术实现。 **应用场景**： 1. **研究与开发**：MoCoGAN模型可用于探索视频生成的新方法，特别是在提升视频合成和预测的质量与效率方面。 2. **教育应用**：作为教学资源，帮助学生深入了解GANs在视频处理领域的应用，包括如何评估生成模型的性能等。 3. **工业应用**：在娱乐、虚拟现实、游戏开发等领域，MoCoGAN可以生成高质量的视频内容，创造新颖的用户体验。 4. **数据分析**：数据科学家可以利用MoCoGAN模拟视频数据，以增强现有数据集或进行数据增强，从而改善机器学习模型的训练效果。 5. **技术评估**：研究人员和开发者可通过MoCoGAN提供的评估指标（如图像分数IS和Frechet Inception Distance, FID）来比较不同模型生成的视频质量。 #### MoCoGAN的关键技术点 1. **Motion and Content Decomposition**：MoCoGAN的核心思想在于将视频中的“内容”与“运动”进行分离。其中，“内容”定义了视频中包含的对象，“运动”则描述了这些对象的动态变化过程。 - 内容部分保持不变，确保视频中对象的一致性； - 运动部分通过随机过程建模，使得生成的视频展现出不同的动态变化。 2. **Unsupervised Learning Scheme**：为了在无监督的情况下学习运动和内容的分解，MoCoGAN引入了一种新型的对抗学习方案。该方案结合了图像和视频判别器，以确保生成的视频既具有真实感又符合预期的内容和运动模式。 3. **Qualitative and Quantitative Evaluation**：通过在多个具有挑战性的数据集上的广泛实验验证了MoCoGAN的有效性。这些实验不仅包括与现有先进技术的定性对比，还涉及定量指标的评估，如图像分数IS和FID等。 4. **Flexible Generation Capabilities**：MoCoGAN的一个显著特点是其灵活的生成能力。它不仅可以生成具有相同内容但不同运动模式的视频，也可以生成具有不同内容但相同运动模式的视频。 MoCoGAN不仅为视频生成领域带来了创新的方法论，还在理论和技术层面上展示了其优越性和灵活性。随着技术的不断发展，MoCoGAN有望成为视频生成技术发展的重要里程碑之一。

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MoCoGAN: Decomposing Motion and Content for Video Generation

Sergey Tulyakov,

Snap Research

stulyakov@snap.com

Ming-Yu Liu, Xiaodong Yang, Jan Kautz

NVIDIA

{mingyul,xiaodongy,jkautz}@nvidia.com

Abstract

Visual signals in a video can be divided into content and

motion. While content speciﬁes which objects are in the

video, motion describes their dynamics. Based on this prior,

we propose the Motion and Content decomposed Genera-

tive Adversarial Network (MoCoGAN) framework for video

generation. The proposed framework generates a video by

mapping a sequence of random vectors to a sequence of

video frames. Each random vector consists of a content

part and a motion part. While the content part is kept

ﬁxed, the motion part is realized as a stochastic process. To

learn motion and content decomposition in an unsupervised

manner, we introduce a novel adversarial learning scheme

utilizing both image and video discriminators. Extensive

experimental results on several challenging datasets with

qualitative and quantitative comparison to the state-of-the-

art approaches, verify effectiveness of the proposed frame-

work. In addition, we show that MoCoGAN allows one to

generate videos with same content but different motion as

well as videos with different content and same motion.

1. Introduction

Deep generative models have recently received an in-

creasing amount of attention, not only because they provide

a means to learn deep feature representations in an unsuper-

vised manner that can potentially leverage all the unlabeled

images on the Internet for training, but also because they

can be used to generate novel images necessary for various

vision applications. As steady progress toward better image

generation is made, it is also important to study the video

generation problem. However, the extension from gener-

ating images to generating videos turns out to be a highly

challenging task, although the generated data has just one

more dimension – the time dimension.

We argue video generation is much harder for the fol-

lowing reasons. First, since a video is a spatio-temporal

recording of visual information of objects performing var-

ious actions, a generative model needs to learn the plausi-

ble physical motion models of objects in addition to learn-

ing their appearance models. If the learned object motion

Content subspace Motion subspace

Motion 1 Motion 2

Figure 1: MoCoGAN adopts a motion and content decom-

posed representation for video generation. It uses an image

latent space (each latent code represents an image) and di-

vides the latent space into content and motion subspaces.

By sampling a point in the content subspace and sampling

different trajectories in the motion subspace, it generates

videos of the same object performing different motion. By

sampling different points in the content subspace and the

same motion trajectory in the motion subspace, it generates

videos of different objects performing the same motion.

model is incorrect, the generated video may contain objects

performing physically impossible motion. Second, the time

dimension brings in a huge amount of variations. Consider

the amount of speed variations that a person can have when

performing a squat movement. Each speed pattern results

in a different video, although the appearances of the human

in the videos are the same. Third, as human beings have

evolved to be sensitive to motion, motion artifacts are par-

ticularly perceptible.

Recently, a few attempts to approach the video genera-

tion problem were made through generative adversarial net-

works (GANs) [12]. Vondrick et al. [40] hypothesize that a

video clip is a point in a latent space and proposed a VGAN

framework for learning a mapping from the latent space to

arXiv:1707.04993v2 [cs.CV] 14 Dec 2017

video clips. A similar approach was proposed in the TGAN

work [30]. We argue that assuming a video clip is a point

in the latent space unnecessarily increases the complexity

of the problem, because videos of the same action with dif-

ferent execution speed are represented by different points

in the latent space. Moreover, this assumption forces ev-

ery generated video clip to have the same length, while the

length of real-world video clips varies. An alternative (and

likely more intuitive and efﬁcient) approach would assume

a latent space of images and consider that a video clip is

generated by traversing the points in the latent space. Video

clips of different lengths correspond to latent space trajec-

tories of different lengths.

In addition, as videos are about objects (content) per-

forming actions (motion), the latent space of images should

be further decomposed into two subspaces, where the devi-

ation of a point in the ﬁrst subspace (the content subspace)

leads content changes in a video clip and the deviation in

the second subspace (the motion subspace) results in tem-

poral motions. Through this modeling, videos of an action

with different execution speeds will only result in different

traversal speeds of a trajectory in the motion space. Decom-

posing motion and content allows a more controlled video

generation process. By changing the content representation

while ﬁxing the motion trajectory, we have videos of dif-

ferent objects performing the same motion. By changing

motion trajectories while ﬁxing the content representation,

we have videos of the same object performing different mo-

tion as illustrated in Fig. 1.

In this paper, we propose the Motion and Content de-

composed Generative Adversarial Network (MoCoGAN)

framework for video generation. It generates a video clip

by sequentially generating video frames. At each time step,

an image generative network maps a random vector to an

image. The random vector consists of two parts where the

ﬁrst is sampled from a content subspace and the second is

sampled from a motion subspace. Since content in a short

video clip usually remains the same, we model the content

space using a Gaussian distribution and use the same real-

ization to generate each frame in a video clip. On the other

hand, sampling from the motion space is achieved through

a recurrent neural network where the network parameters

are learned during training. Despite lacking supervision re-

garding the decomposition of motion and content in nat-

ural videos, we show that MoCoGAN can learn to disen-

tangle these two factors through a novel adversarial train-

ing scheme. Through extensive qualitative and quantitative

experimental validations with comparison to the state-of-

the-art approaches including VGAN [40] and TGAN [30],

as well as the future frame prediction methods including

Conditional-VGAN (C-VGAN) [40] and Motion and Con-

tent Network (MCNET) [39], we verify the effectiveness of

MoCoGAN.

1.1. Related Work

Video generation is not a new problem. Due to limita-

tions in computation, data, and modeling tools, early video

generation works focused on generating dynamic texture

patterns [34, 41, 9]. In the recent years, with the availability

of GPUs, Internet videos, and deep neural networks, we are

now better positioned to tackle this intriguing problem.

Various deep generative models were recently proposed

for image generation including GANs [12], variational au-

toencoders (VAEs) [20, 28, 36], and PixelCNNs [38]. In

this paper, we propose the MoCoGAN framework for video

generation, which is based on GANs.

Multiple GAN-based image generation frameworks

were proposed. Denton et al. [8] showed a Laplacian pyra-

mid implementation. Radford et al. [27] used a deeper con-

volution network. Zhang et al. [43] stacked two generative

networks to progressively render realistic images. Coupled

GANs [22] learned to generate corresponding images in dif-

ferent domains, later extended to translate an image from

one domain to a different domain in an unsupervised fash-

ion [21]. InfoGAN [5] learned a more interpretable latent

representation. Salimans et al. [31] proposed several GAN

training tricks. The WGAN [3] and LSGAN [23] frame-

works adopted alternative distribution distance metrics for

more stable adversarial training. Roth et al. [29] proposed

a special gradient penalty to further stabilize training. Kar-

ras et al. [18] used progressive growing of the discriminator

and the generator to generate high resolution images. The

proposed MoCoGAN framework generates a video clip by

sequentially generating images using an image generator.

The framework can easily leverage advances in image gen-

eration in the GAN framework for improving the quality of

the generated videos. As discussed in Section 1, [40, 30]

extended the GAN framework to the video generation prob-

lem by assuming a latent space of video clips where all the

clips have the same length.

Recurrent neural networks for image generation were

previously explored in [14, 16]. Speciﬁcally, some works

used recurrent mechanisms to iteratively reﬁne a generated

image. Our work is different to [14, 16] in that we use

the recurrent mechanism to generate motion embeddings

of video frames in a video clip. The image generation is

achieved through a convolutional neural network.

The future frame prediction problem studied in [33, 26,

24, 17, 10, 37, 42, 39, 7] is different to the video gen-

eration problem. In future frame prediction, the goal is

to predict future frames in a video given the observed

frames in the video. Previous works on future frame pre-

diction can be roughly divided into two categories where

one focuses on generating raw pixel values in future frames

based on the observed ones [33, 26, 24, 17, 42, 39], while

the other focuses on generating transformations for reshuf-

ﬂing the pixels in the previous frames to construct fu-

ture frames [10, 37]. The availability of previous frames

makes future frame prediction a conditional image gener-

ation problem, which is different to the video generation

problem where the input to the generative network is only a

vector drawn from a latent space. We note that [39] used a

convolutional LSTM [15] encoder to encode temporal dif-

ferences between consecutive previous frames for extract-

ing motion information and a convolutional encoder to ex-

tract content information from the current image. The con-

catenation of the motion and content information was then

fed to a decoder to predict future frames.

1.2. Contributions

Our contributions are as follows:

1. We propose a novel GAN framework for unconditional

video generation, mapping noise vectors to videos.

2. We show the proposed framework provides a means to

control content and motion in video generation, which is

absent in the existing video generation frameworks.

3. We conduct extensive experimental validation on bench-

mark datasets with both quantitative and subjective com-

parison to the state-of-the-art video generation algo-

rithms including VGAN[40] and TGAN [30] to verify

the effectiveness of the proposed algorithm.

2. Generative Adversarial Networks

GANs [12] consist of a generator and a discriminator.

The objective of the generator is to generate images resem-

bling real images, while the objective of the discriminator

is to distinguish real images from generated ones.

Let x be a real image drawn from an image distribution,

, and z be a random vector in Z

≡ R

. Let G

and D

be the image generator and the image discriminator. The

generator takes z as input and outputs an image,

x = G

(z),

that has the same support as x. We denote the distribution

of G

(z) as p

. The discriminator estimates the probability

that an input image is drawn from p

. Ideally, D

(x) = 1

if x ∼ p

and D

(

x) = 0 if

x ∼ p

. Training of G

and

is achieved via solving a minimax problem given by

max

min

, G

) (1)

where the functional F

is given by

, G

) = E

x∼p

[− log D

(x)] +

z∼p

[− log(1 − D

(z)))]. (2)

In practice, (1) is solved by alternating gradient update.

Goodfellow et al. [12] show that, given enough capacity

to D

and G

and sufﬁcient training iterations, the distri-

bution p

converges to p

. As a result, from a random

vector input z, the network G

can synthesize an image that

resembles one drawn from the true distribution, p

2.1. Extension to Fixed-length Video Generation

Recently, [40] extended the GAN framework to video

generation by proposing a Video GAN (VGAN) framework.

Let v

= [x

(1)

, ..., x

(L)

] be a video clip with L frames.

The video generation in VGAN is achieved by replacing

the vanilla CNN-based image generator and discriminator,

and D

, with a spatio-temporal CNN-based video gener-

ator and discriminator, G

and D

. The video generator

maps a random vector z ∈ Z

≡ R

to a ﬁxed-length

video clip,

= [

(1)

, ...,

(L)

] = G

(z) and the video

discriminator D

differentiates real video clips from gen-

erated ones. Ideally, D

) = 1 if v

is sampled from

and D

(

) = 0 if

is sampled from the video

generator distribution p

. The TGAN framework [30]

also maps a random vector to a ﬁxed length clip. The dif-

ference is that TGAN maps the random vector, representing

a ﬁxed-length video, to a ﬁxed number of random vectors,

representing individual frames in the video clip and uses an

image generator for generation. Instead of using the vanilla

GAN framework for minimizing the Jensen-Shannon diver-

gence, the TGAN training is based on the WGAN frame-

work [3] and minimizes the earth mover distance.

3. Motion and Content Decomposed GAN

In MoCoGAN, we assume a latent space of images Z

≡

where each point z ∈ Z

represents an image, and a

video of K frames is represented by a path of length K in

the latent space, [z

(1)

, ..., z

(K)

]. By adopting this formula-

tion, videos of different lengths can be generated by paths of

different lengths. Moreover, videos of the same action ex-

ecuted with different speeds can be generated by traversing

the same path in the latent space with different speeds.

We further assume Z

is decomposed into the content

and motion Z

subspaces: Z

= Z

× Z

where

= R

, Z

= R

, and d = d

+ d

. The content

subspace models motion-independent appearance in videos,

while the motion subspace models motion-dependent ap-

pearance in videos. For example, in a video of a person

smiling, content represents the identity of the person, while

motion represents the changes of facial muscle conﬁgura-

tions of the person. A pair of the person’s identity and the

facial muscle conﬁguration represents a face image of the

person. A sequence of these pairs represents a video clip

of the person smiling. By swapping the look of the person

with the look of another person, a video of a different person

smiling is represented.

We model the content subspace using a Gaussian distri-

bution: z

∼ p

≡ N (z|0, I

) where I

is an identity

matrix of size d

× d

. Based on the observation that the

content remains largely the same in a short video clip, we

use the same realization, z

, for generating different frames

in a video clip. Motion in the video clip is modeled by a

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