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Human Pose Transfer with Augmented Disentangled Feature Consistency

Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguity and appearance inconsistency. To alleviate the current limitations and improve the quality of the synthesized images, we propose a pose transfer network with augmented Disentangled Feature Consistency (DFC-Net) to facilitate human pose transfer. Given a pair of images containing the source and target person, DFC-Net extracts pose and static information from the source and target respectively, then synthesizes an image of the target person with the desired pose from the source. Moreover, DFC-Net leverages disentangled feature consistency losses in the adversarial training to strengthen the transfer coherence and integrates a keypoint amplifier to enhance the pose feature extraction. With the help of the disentangled feature consistency losses, we further propose a novel data augmentation scheme that introduces unpaired support data with the augmented consistency constraints to improve the generality and robustness of DFC-Net. Extensive experimental results on Mixamo-Pose and EDN-10k have demonstrated DFC-Net achieves state-of-the-art performance on pose transfer.


I. INTRODUCTION
H UMAN pose transfer has become increasingly compelling recently since it is closely related to movies' special effects [1], reenactment [2], person re-identification [3], entertainment systems [4] and so forth [5], [6]. Given some images of a target person and a source person image with the desired pose (e.g., judo, dance), the goal of the human pose transfer task is to synthesize a realistic image of the target person with the desired pose of the source person.
With the power of deep learning, especially the generative adversarial networks (GANs) [7], pioneering works have raised impressive solutions to address the human image generation [8], [9], [10], [6] by efficiently leveraging the image-to-image translation schemes and have achieved significant progress. Intuitively, early routine coarsely conducts human pose transfer through general image-to-image translation methods such as Pix2Pix [11] and CycleGAN [12], which attempt to translate the extracted skeleton image of source person to the image of target person with the desired poses. Subsequent approaches [13], [14], [15] adopt specifically designed modules for human pose transfer. Specifically, the Unet architecture with skip connections in [16] is employed to keep the low-level features. To mitigate the pose misalignment between the source and target persons, [17] uses part-wise affine transformation with a modified feature fusion mechanism to warp the appearance features onto the target pose. Later, extensive works have been presented to strengthen the modeling ability of body deformation and feature transfer with different methods, including 3D surface models [18], [15], [19], local attention [20], [21] and optical flow [22]. However, the warping methods commonly struggle with pose ambiguity when viewpoint changes, occlusions occur, or even transferring a complicated pose in many situations. To address the pose ambiguity, a series of works [22], [23] use predictive branch to illuminate and replenish new contents for invisible regions. When the hallucinated contents have a different context style than the local-warped ones, generated images will have a low visual fidelity due to appearance inconsistency. One of the main reasons for pose ambiguity and appearance inconsistency is that the commonly used reconstruction loss and the adversarial generative loss only constrain the synthesized image generation in image-level.
Towards alleviating the mentioned limitations, it is important to disentangle the pose and appearance information, and exploit the disentangled pose and appearance feature consistencies between the synthesized and real images, i.e., the synthesized target image should have a similar high-level appearance feature to the real target person as well as a similar high-level pose feature to the real source person. The disentangled pose and appearance feature consistencies can constrain the training in feature-level and lead to a more consistent and realistic synthesized result.
In this paper, we propose a pose transfer network with augmented Disentangled Feature Consistency (DFC-Net) to facilitate human pose transfer. DFC-Net contains a pose feature encoder and a static feature encoder to extract pose and appearance features from the source and target person, respectively. In pose feature encoder, we integrated a pretrained pose estimator such as OpenPose [24] to extract the keypoint heatmaps. Notice that the pose estimator is pretrained on COCO keypoint challenge dataset [25], which is not any dataset deployed in our experiments. Further, in order to remedy the distortion of the extracted keypoints caused by the distribution shift from pose estimator, we introduce a keypoint amplifier to eliminate the noise in keypoint heatmaps. An image generator synthesizes a realistic image of the target person conditioned on the disentangled pose and appearance features. The feature encoders and image generator empower DFC-Net to enable us to present novel feature-level pose and appearance consistency losses [12]. These losses reinforce the consistency of pose and appearance information in the feature space and simultaneously maintain visual fidelity. Additionally, to further improve the robustness and generality of DFC-Net, by disentangling the pose information from different source persons, we present a novel data augmentation scheme that builds an extra unpaired support dataset as the source images, which provides different persons with unseen poses in the training set and augmented consistency constraints.
We also notice that the commonly used datasets and benchmarks [26], [27] only focus on the persons in the real world with a low-resolution, which is not sufficient to verify the effectiveness of each algorithm on other domains, such as computer animation [28]. Inspired by [28], we collect an animation character image dataset named MIXAMO-POSE from Adobe Mixamo [29], a 3D animation library, to accurately generate different characters performing the identical poses as a benchmark to assess the human pose transfer between different people. MIXAMO-POSE contains four different animation characters performing 15 kinds of poses. To further evaluate the DFC-Net, we also modify a real person dataset called EDN-10K upon [30], which contains 10K high-resolution images for four real subjects performing different poses. The experimental results on these two datasets demonstrate that our model can effectively synthesize realistic images and conduct pose transfer for both the animation characters and real persons.
In summary, our contributions are as follows: • We propose a novel method DFC-Net for human pose transfer with two disentangled feature consistency losses to make the information between the real images and synthesized images consistent. • We propose a novel data augmentation scheme that enforces augmented consistency constraints with an unpaired support dataset to further improve the generality of our model. •  II. RELATED WORK Generative adversarial networks (GANs) [7] have achieved tremendous success in image-to-image translation tasks, whose goal is to convert images from one domain to a different domain. Pix2Pix [11] proposes a framework based on cGANs [31] with an encoder-decoder architecture [32]; Cycle-GAN [12] addressed this problem by using a cycle-consistent GANs; DualGAN [33] and [34] are also unsupervised imageto-image translation methods trained on unpaired dataset. Similarly, [35], [36], [37] are also image-to-image translation techniques, but they try to generate a dataset of the target domain with labels for domain adaptation tasks. The above works can be exploited as a general approach in the human pose transfer task, while the precondition is that they have a specific image domain which can be converted to the synthesized image domain, e.g., using a pose estimator [38] to generate a paired skeleton image dataset. Different from the image-to-image translation methods, DFC-Net improved the quality of the synthesized image by adding consistency constraints in the feature space.
Recently, there is a growing number of human pose transfer methods with specifically designed modules. One branch is the spatial transformation methods [17], [39], [15], aiming to build the deformation mapping of the keypoint correspondences in the human body. By leveraging the spatial transformation capability of CNN, [40] presented the spatial transformer networks (STN) that approximate the global affine transformation to warp the features. Following STN, several variant works [41], [42], [43] have been proposed to synthesize images with better performance. [44] used image segmentation to decompose the problem into modular subtasks for each body part and then integrated all parts into the final result. [17] built deformable skip connections to move information and transfer textures for pose transfer. Monkey-Net [45] encoded pose information via dense flow fields generated from keypoints learned in a self-supervised fashion. First-Order Motion Model [46] decoupled appearance and pose and proposes to use learned keypoints and local affine transformations to generate image animation. [23] integrated the human pose transfer, appearance transfer and novel view synthesis into one unified framework by using SMPL [47] to generate human body mesh. The spatial transformation methods usually implicitly assume that the warping operation can cover the whole body. However, when viewpoint changes and occlusions occur, the above assumption can not hold, leading to pose ambiguity and performance dropping.
Another branch methods are pose-guided and aim to predict new appearance contents in uncovered regions to handle the pose ambiguity problem. One of the earliest works, PG 2 [13], presented a two-stage method using U-Net to synthesize the target person with arbitrary poses. [14] further decomposed the image into the foreground, background and pose features to achieve more precise control of different information. [48] introduced a multi-stage GAN loss and synthesized each body part, respectively. [18] leveraged the DensePose [49] rather than the commonly used 2D key-points to perform accurate pose transfer. [30] learned a direct mapping from the skeleton images to synthesized images with corresponding pose based on the architecture of Pix2PixHD [50]. PATH [20] introduced cascaded attention transfer blocks (PATBs) to refine pose and appearance features simultaneously. Inspired by PATH, PMAN [51] proposed a progressive multi-attention framework with memory network to improve the image quality. However, some of these methods [18], [52], [22] focused on synthesizing results at the image level (i.e., adversarial and reconstruction losses), thus leading to appearance inconsistency when predicted local contents are not consistent with the surrounding contexts.
In contrast, our method learns to disentangle and reassemble the pose and appearance in the feature space. One similarly work close to ours is C 2 GAN [53] which consists of three generation cycles (i.e., one for image generation and two for keypoint generation). C 2 GAN explored the cross-modal information in the image-level at the cost of model complexity and training instability while DFC-Net only introduced two feature consistency losses into the full objective, which kept the model simple and effective. By disentangling the pose and appearance features, we can enforce the feature consistencies between the synthesized and real images, and leverage the pose features from an unpaired dataset to improve performance.

A. Overview
Given one image x s of a source person and another image x t of a target person, DFC-Net synthesizes an image x syn , which reserves a) the pose information, e.g., pose and location, of the source person in x s , and b) the static information, e.g., person appearance and environment background, from the target image x t . For each image, DFC-Net attempts to disentangle the pose and static information into orthogonal features. Specifically, DFC-Net consists of the following core components: 1) a Pose Feature Encoder M (·), which extracts pose features M (x) from an image x; 2) a Static Feature Encoder S(·), which extracts static features S(x ) from an image x ; and 3) an Image Generator G(·), which synthesizes an image G(M (x), S(x )) based on the encoded pose and static features M (x) and S(x ) from images x and x separately.
In the remainder of this section, we describe the model architecture and introduce the training procedure, followed by the model instantiations.

B. Pose Transfer Network Architecture
The architecture of the proposed model is shown in Figure 1. 1) Pose Feature Encoder: Our designed Pose Feature Encoder consists of a Pose Estimator network, a Keypoint Amplifier block, and a Pose Refiner network. Given a RGB image x ∈ R 3×H×W of height H and width W , the pre-trained Pose Estimator aims at extracting pose information P (x) from the image x. Similar to [38], the extracted pose information contains the downsampled keypoint heatmaps h ∈ R 18× H 8 × W 8 and the part affinity fields p ∈ R 38× H 8 × W 8 . The keypoint heatmaps h store the heatmaps of 18 body parts, and the part affinity fields p store the location and orientation for heatmaps of body parts and background, which has 38 (= (18 + 1) × 2) channels.
As the pre-trained pose estimator (OpenPose [24] in our implementation) is pre-trained on COCO keypoint challenge dataset [25], when applied on MIXAMO-POSE and EDN-10K datasets with different distributions, more noise are made on keypoint heatmaps h. To reduce the interference of noise, we apply a softmax function with a relatively small temperature T (e.g., 0.01) as the Keypoint Amplifier to denoise the extracted keypoint heatmaps by increasing the gap between large and small values in the heatmaps and obtain the amplified heatmaps h by By employing Keypoint Amplifier on the input heatmaps, the small probability, e.g., 0.2, will be squeezed to almost 0.0. On the contrary, the large probability, e.g., 0.8, will be squeezed to almost 1.0. Without the Keypoint Amplifier, the generator may still synthesize blurry limbs for the low probability areas and twist the generated person.
Finally, the Pose Refiner takes both the part affinity fields p and the amplified keypoint heatmaps h and produces the encoded pose feature vector M (x). In this way, the pose information extracted from the Pose Estimator can be refined, and the influence caused by different limb ratios and/or camera angles and distances can be reduced.
2) Static Feature Encoder: While the Pose Feature Encoder is not capable of extracting static information, the static information including background, personal appearance, etc., from another image x , are captured automatically by another module with the help of the full objective function. Named as Static Feature Encoder, this module extracts only static features S(x ) from x .
3) Image Generator: Given pose features M (x s ) extracted from a source image x s and static features S(x t ) extracted from a target image x t , the Image Generator outputs the synthesized image x syn by It is noting that many existing methods (e.g., [30]) attempt to learn pose-to-image or pose-to-appearance mapping solely via its generator, which is a difficult task on parameterizing the static information of the target in the generator. Meanwhile, with the same generator, the desired pose information of x s may fail to synthesize if the desired pose is very different from the training poses. DFC-Net, instead, extracts and utilizes decomposed static and pose features, which enables the reconstruction's quality improvement.

C. Training DFC-Net
We train the pose transfer network in an adversarial learning way with disentangled feature consistency losses as well as other objectives. Basically, the model is trained with a set of images of the same person, possibly from one or several video clips. To further improve the generalization ability of DFC-Net, we propose to train DFC-Net with a support set and the augmented consistency losses. We show the ablation study results in Section IV-C.
1) Adversarial Training: We employ an Image Discriminator (D) in an adversarial learning way to ensure the synthesized image x syn borrows the pose and static information from the source and target images (x s and x t ) separately. As both the source and target images during training contain the same person with the same appearance and background, they share almost the same static features, i.e., S(x t ) S(x s ). Therefore, the output of the model x syn can also be treated as a reconstruction of the source image x s , as the synthesized images contain the same pose features M (x s ) as the source image. This inspires us to resort to the conditional generative adversarial network (cGAN) [11], where the Image Discriminator attempts to discern between the real sample x s and the generated image x syn , conditioned on the pose features M (x s ) extracted from the source image. That is, the Image Discriminator attempts to fit D(x s , M (x s )) = 1 and D(x syn , M (x s ))) = 0. The adversarial loss is described as follows: where We enhance the Image Discriminator with a multi-scale discriminator D = (D 1 , D 2 ) [50] and include the discriminator feature matching loss L fm in our objective. The feature matching loss is a weighted sum of feature losses from 5 different layers of the Image Discriminator, calculated by L 1 distance between the corresponding features of x s and x syn .
In order to increase the training stability and improve the synthesized image quality, we also add the perceptual loss L per [54] based on a pre-trained VGG network [55].
2) Disentangled Feature Consistency Losses: The above adversarial training losses aim at penalizing discrepancy between the synthesized and source images directly in the raw image space. To improve the accuracy and robustness of the pose transfer results, we also introduce two disentangled feature consistency losses in terms of pose and static features to assure the synthesized person looks like the target person and behaves as the source person separately. The pose consistency loss L mc measures the differences between the synthesized and source images in the pose feature space, and the static consistency loss L sc measures the differences between the synthesized and target images in the static feature space. They are both L 1 distances between the outputs from the corresponding encoders, formally defined as 3) Augmented Consistency Loss: Through disentangling the pose feature from the source images, we find that images with different persons can also be passed into the training process as the source images x s to improve the generalization ability of our model. Hence, we introduce a novel data augmentation method that extends the training dataset with the images of different persons, referred to as the support set, providing many kinds of unseen poses. Note that the subjects in support set can be arbitrary and are different from the primary training dataset, so the ground-truth images with the target person performing the pose of the source person are not available at all. As a result, the corresponding losses L + adv , L per and L fm for the support set are not applicable, and we optimize relevant objective terms, which is defined by where the weights λ adv , λ mc , λ sc are the weights for each loss. 4) Full Objective: By bringing all the objective terms together, we train all components jointly except for the Pose Estimator to minimize the full objective L full below.
L full =λ adv L adv + λ fm L fm + λ per L per (9) + λ mc L mc + λ sc L sc + L sup where λ adv , λ fm , λ per are set to 1, 10, 10 following Pix2Pix [11] and Everybody Dance Now (EDN) [30], while λ mc , λ sc are set to 0.1, 0.01 by grid search. We set the λ sc to 0.01 comparing to the λ mc to balance the L mc and L sc .

D. Training and Inference Process
For each subject in the training dataset (i.e., MIXAMO-POSE and EDN-10K in our experiments), we train one separate model following the same scheme of [30] (e.g., we trained four models for four subjects in EDN-10k dataset.) For the sake of comparison fairness, we also train all the baseline methods following the same scheme.
During the training stage, given a training dataset consisting of N images of one subject and a support set, for each training iteration, we randomly choose a pair of images as x s and x t from the training dataset and an image as x s from the support set respectively, pass them into the DFC-Net and train it using the full objective in Equation 9.
During the inference stage, given a desired pose image x s , we randomly choose an image x t from the training dataset and synthesize the result.

E. Implementation details
We employed the pre-trained VGG-19 [56] network part from [38] for the Pose Estimator, and adopted the similar approach in [22] to build our network, the detailed designs are as follows: • Pose Refiner: It is composed of a convolutional block, a channel-wise upsampling module and five residual blocks [57]. Firstly, the convolutional block consists of a reflection padding layer, a 7 × 7 convolutional layer, a batch normalization layer and ReLU. The channel-wise upsampling module, which increases the number of the channel from 64 to 512, contains three convolutional blocks. Each block contains a 3 × 3 convolutional layer, a batch normalization layer and ReLU. Each of the five residual blocks consists of two small convolutional blocks and each block has a reflection padding layer, a 3 × 3 convolutional layer, a batch normalization layer. The first small convolutional block also has a ReLU at the end. • Static Feature Encoder: It firstly has the same convolutional block as in the Pose Refiner. Then it contains three convolutional downsampling blocks and each block consists of a 3×3 convolutional layer, a batch normalization layer and ReLU. There are also five residual blocks following the downsampling blocks as the same as in the Pose Refiner. • Image Generator: It is composed of four residual blocks, an upsampling module and a convolutional block. Each of the residual blocks is the same as in the Pose Refiner and Static Feature Encoder. The upsampling module consists of three transposed convolutional blocks and each block is composed of a 3 × 3 transposed convolutional layer, a batch normalization layer and ReLU. The last convolutional block contains two reflection padding layers, two 7 × 7 convolutional layers and a tangent function. • Image Discriminator: It contains two discriminators at different scales, which are similar to [50]. For each discriminator it is composed of five convolutional blocks.
The first block has a 4 × 4 convolutional layer and LeakyReLU. Each of the next three blocks has a 4×4 convolutional layer, a batch normalization layer and Leaky ReLU. The last block only has a 4×4 convolutional layer.

IV. EXPERIMENTS
A. Experimental Setup 1) Datasets: We built MIXAMO-SUP as support set for data augmentation, to boost the generality of DFC-Net and organized two datasets, MIXAMO-POSE and EDN-10K, to verify the effectiveness of the proposed DFC-Net for human pose transfer.
• MIXAMO-POSE: We randomly chose 4 characters, Andromeda, Liam, Remy and Stefani, with 30 different pose sequences from Mixamo. To render the 3D animations into 2D images, we loaded each character performing each pose sequence on a white background into Blender [58], placed two cameras in front of and behind the character, and took the images. We centered the characters in the images according to their keypoints and resized them to 256×256. MIXAMO-POSE were splitted into training and test sets. For each character, the training set contains 1488 images with 15 poses, and the test set contains 1185 images with 15 other poses. • EDN-10K: We processed and tailored the dataset released by [30]      contain the same person as the target person image, DFC-Net leveraged the support set as the source person images x s . When training on both EDN-10K and MIXAMO-POSE, we use MIXAMO-SUP as the support set. Note that MIXAMO-SUP has a totally different distribution from EDN-10K but still gains a huge improvement shown in Section IV-C. Note that the experiments on EDN-10K only include the pose transfer on the same person because the ground-truth of different people carrying the same pose are unavailable. For MIXAMO-POSE, since we can manipulate different characters to do the same action, the experiments include the pose transfer both on the different people, e.g., transfer the unseen pose of Liam to Andromeda, and the same person, e.g., transfer the unseen pose of Andromeda to herself.
2) Baseline Methods: We compared our DFC-Net with the following competitive baselines: • Nearest Neighbors (NN): For each source person image x s , we chose the image x in the training set D tr with the lowest mean square error (MSE) between the pose information P (x s ) and P (x ) as x syn .
The pose information was extracted by the same Pose Estimator as in our method. • Pose-guided Methods: We chose CycleGAN [12], Pix2Pix [11] and Everybody Dance Now (EDN) [30] as the baselines. They all took the skeleton images as input instead of the original images, we employed a pretrained pose estimator [38] to extract keypoints, used OpenCV [59] to connect pairs of keypoints with different colors to generate the skeleton images. To ensure fair comparisons, the face GAN and face keypoint estimator in EDN were not adopted in our implementation, as they are independent components and can be seamlessly adopted by other learning-based baselines. • Spatial Transformation Methods: We selected Liquid Warping GAN (LWG) [23], Monkey-Net (MKN) [45] and First Order Motion Model (FOMM) [46]. LWG calculates the flow fields with additional 3D human models and integrates the human pose transfer, appearance transfer and novel view synthesis into one unified framework. MKN and FOMM are both object-agnostic frameworks using learned keypoints to generate image animation in a self-supervised fashion.

3) Evaluation Metrics:
We evaluated the quality of the synthesized images with three commonly used metrics: • MSE: The mean squared error between the values of pixels of synthesized images and ground-truth images.
Lower MSE values are better. • PSNR: The peak signal-to-noise ratio, which provides an empirical measure of the quality of synthesized images regarding ground-truth images. Higher PSNR values are better. • SSIM: Structural similarity [60], which is another perceptual metric that quantifies the quality of synthesized images given ground-truth images and focuses more on structural information (e.g., light). Higher SSIM values are better.
We calculated the average scores among all pairs of synthesized and ground-truth images on the test set. On MIXAMO-POSE, for each character as the target person, we reported the average metrics of 4 different characters as the source person. While on EDN-10K, we reported metrics of the task on the same person for every subject.

C. Ablation Study
To better understand the merits of designs of DFC-Net, we conducted detailed ablation studies on Subject1 from EDN-10K and Liam from MIXAMO-POSE. The evaluation results are shown in Tables IX and X. 1) Keypoint Amplifier: The results in the second row versus those in the first row showed that the keypoint amplifier strengthened the performance of pose transfer. Even with the consistency losses and support set (rows 6 and 7), it continuously reduced the interference of the noise on the keypoint heatmaps. 2) Consistency Losses: We explored the effects of consistency losses on the task of pose transfer. Considering the different results between the second row and the third row, the static feature consistency loss L sc boosted the pose transfer task and verified its validity. Moreover, the performance variations between the second row and the fourth row clearly evidenced the advantages of pose feature consistency loss L mc .
3) Support Set with Augmented Consistency Loss: Finally, we added the support set and utilized the augmented consistency loss L sup during the training. The comparisons among the final row and other rows notably indicated that the support set could improve the generalization ability and the robustness of our model, especially when the poses of the source person are closer to the poses in the support set. The support set provided more negative examples besides the original training set and could help the discriminator to form an accurate boundary, which could further strengthen the performance of the generator.

D. Qualitative Results
We further highlighted the superiority of our proposed approach by showing and contrasting the visualizations of synthesized results of all models on MIXAMO-POSE and EDN-10K, respectively. In addition, we presented the visualizations of the ablation comparisons for Subject1 on EDN-10K to indicate the effectiveness of components of DFC-Net.
1) Visual comparisons on MIXAMO-POSE: As depicted in Figure 2, we employed multiple target persons and source persons with various poses that were different from those in the training set of the MIXAMO-POSE. Compared with the aforementioned baselines, DFC-Net offered better transfer results and alleviated their drawbacks on synthesizing body parts, clothes, faces, just name a few. We illustrate details as follows: • Firstly, only DFC-Net generated the left hand of Liam at the 1st and 2nd rows while there were only the arm in results of other baselines. • Meanwhile, images produced by EDN had stale colors and lots of noisy pixels in terms of clothes and faces, and it failed to capture the details of shoes at the 3rd row since the color of shoes is white and is easily confused with the background.   • The CycleGAN could provide similar poses compared with the ground-truths, the poses were distorted and the qualities of corresponding images were constantly degraded. • Besides, though the Pix2Pix and EDN could synthesize the target persons with the desired poses, some body parts (e.g., arms, hands) generated by Pix2Pix were severely corrupted. 2) Visual comparisons on EDN-10K: We also provided visualization results on EDN-10K in Figure 3 and cropped the results in Figure 4 with more details, and DFC-Net synthesized real person image results with better qualities, which clearly indicated the effectiveness of our method as well. For instance: • The results produced by Pix2Pix and EDN were more blurry and there was an aliasing effect on the edges of the subject in the Pix2Pix result. • Sometimes NN could generate images that are closer to the ground-truths, e.g. image of Subject3 at the third row of Figure 3, since the number of images in the training set increases from 1488 to 10000. But people could still easily determine that the poses of the generated images are different from the ground-truths. • In addition, EDN showed better image results than Pix2Pix did. In the third row of Figure 3, we noticed that the image of Subject3, synthesized by Pix2Pix, had much noise in the subject's hair. On the contrary, the result of EDN was smoother and more realistic, but they lacked many details compared with DFC-Net, especially the face part was still twisted. • As depicted in Figure 4, we observed that the synthesized images of our method contained more details, including the clear face, wrinkles on clothes, etc. 3) Visual ablation studies on Subject1: We presented the visualization of the ablation study results on Subject1 in Figure 5. In which we observed that each component has different degrees of improvement to the results, including clear head, hand gesture, etc. Specifically: • As shown in the column of setting 7, DFC-Net with full components synthesized images with more accurate details and better qualities. • The results produced by setting 1, which does not have the Keypoint Amplifier, have much noise then other settings, especially near the head area.

V. CONCLUSION
In this paper, we proposed DFC-Net, a novel network with disentangled feature consistencies for human pose transfer. We  Table X. introduce two disentangled feature consistency losses to enforce the pose and static information to be consistent between the synthesized and real images. Besides, we leverage the keypoint amplifier to denoise the keypoint heatmaps and make it easier to extract the pose features. Moreover, we show that the support set MIXAMO-SUP containing different subjects with unseen poses can boost the pose transfer performance and enhance the robustness of the model. To enable the accurate evaluation of pose transfer between different people, we collect an animation character dataset MIXAMO-POSE. Results on both animation and real image datasets, MIXAMO-POSE and EDN-10K, consistently demonstrated the effectiveness of the proposed model. Zheng Guan is an assistant teacher of Beijing Institute of Technology, graduated from Beijing Institute of Technology Computer Science School. His interest areas are simulation systems, embedded systems, and artificial intelligence.
Gangyi Ding is a professor of Beijing Institute of Technology. His interest areas are simulation systems, embedded systems, and artificial intelligence.