Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images

07/03/2019 ∙ by Yinhao Ren, et al. ∙ 8

Recent advancements in conditional Generative Adversarial Networks (cGANs) have shown promises in label guided image synthesis. Semantic masks, such as sketches and label maps, are another intuitive and effective form of guidance in image synthesis. Directly incorporating the semantic masks as constraints dramatically reduces the variability and quality of the synthesized results. We observe this is caused by the incompatibility of features from different inputs (such as mask image and latent vector) of the generator. To use semantic masks as guidance whilst providing realistic synthesized results with fine details, we propose to use mask embedding mechanism to allow for a more efficient initial feature projection in the generator. We validate the effectiveness of our approach by training a mask guided face generator using CELEBA-HQ dataset. We can generate realistic and high resolution facial images up to the resolution of 512*512 with a mask guidance. Our code is publicly available.

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1 Introduction

The ability to synthesize photo-realistic images from a semantic map is highly desired for various image editing applications. Most existing approaches with semantic mask inputs focus on either applying the coarse to fine synthesis with a cascade of networks Pix2pix_HD ; refinementGAN ; stackGAN

, or designing specific loss functions

wGAN ; wgan_gp to increase the model stability for better image quality. Though advances have been made, it is still challenging to synthesize high resolution images with diverse local features using semantic masks as guidance.

In this work, we propose a novel technique that enables the generative models to synthesize images that are coherent with the provided semantic mask constraint while preserving the diversity of local texture details. This characteristic is especially useful in image generation applications that require high resolutions output, feature diversity and high fidelity. For example, a live art editing interface implemented with this technique would allow content creators to focus on the global concept while the algorithm deals with local details automatically.

Figure 1: Generated image samples and cartoon illustration of the sample space mapping challenge during training a mask guided face generator: An image translation model trained to map the same circle pattern to various different ball patterns. The model is likely to learn the "average" ball pattern of the training dataset instead of been able to map to all of them individually. The same problem exist for training a generator to replicate different faces with very similar mask representation.

Image translation models such as Pix2Pix

pix2pix2016 that directly map the abstract representation of images with the original images using U-Net UNet style generator do not have the proper mechanism for stochastic feature realizations. This usually causes the model to output ambiguous features that are in between possible solutions in the feature space, as shown in Fig 1

. Heuristically, this leads to blurred images and ill-defined texture details. One solution of this problem is proposed by Wang et al. 

Pix2pix_HD (Pix2Pix-HD). They use a coarse to fine approach together with perceptual loss to refine the output image quality. However, this approach requires a much larger model, but still does not solve the fundamental issue of feature mapping. A more theoretically sound solution proposed by Zhao et al. Tube_GAN (Tube-GAN) is to use both a latent vector

(noise in a chosen distribution) and a semantic mask as conditional input, allowing the model to learn the joint distribution. Nevertheless, their proposed merging strategy of projected latent features and projected mask features are not by default guaranteed to be coherent, thus this model is limited to generating only low frequency information in the background. Thus it is still a challenging task to use pixel-level mask as guidance to generate high resolution images with fine details.

In this paper, we address the two main issues of current state-of-art mask guided generative models Pix2pix_HD ; Tube_GAN with pixel-level semantic input: (1) lack of diverse fine-grained texture details in synthesized results, caused by inefficient mask-to-image domain mapping and (2) low parameter efficiency in current multiple conditional inputs architecture designs. For the first issue, we argue that coupling latent vector with the input semantic map leads to better sample space mapping, which results in diverse texture details in synthesized results. For the second issue, our solution is to inject mask embedding into the latent input vector before the initial feature projection, and this operation significantly improves the quality of texture details in synthesized results. Coupling the mask embedding vector with latent vector is an efficient way to add mask constrains, since it allows the initial feature projection to be compatible with the pixel-level mask constraint. Contrary to Tube-GAN we use the projected mask features mainly as a constraint to the latent features so that the up-sampling path of the network is able to preserve most of its capacity to perform refinement of local texture details. Fig 1 provides a preview of our synthesized images and the corresponding input masks. The methods and detailed analysis will be presented below.

2 Related Work

2.1 Conditional GAN

Conditional GANs ConditionalGA achieve the control of generator output through coupling of latent vector and conditional inputs. Many studies cGAN_face_attribute ; cGAN_facial_expression ; cGAN_face_aging ; cGAN_histology ; cGAN_brain_tumor ; cGAN_MRI applied cGAN using image attributes in vector form (such as labels) for controlled image synthesis. Pix2Pix pix2pix2016 and Pix2Pix-HD Pix2pix_HD first proposed to use semantic input directly in an encoder-decoder style structure for image-to-image translation. Some studies have applied input embedding to transform a higher dimensional attribute such as semantic mask into a more compact lower dimensional form. CCGAN text_embedding proposes using sentence embedding that contains image characteristics as feature for cycle GAN training. Their study shows condensed text information can be merged with generator latent features as conditions for image synthesis. Yildirim et al.disentanglement uses the binary mask embedding as part of the conditional input to control the shape of generated garment images. However, their work indicates mask embedding vector is not sufficient for pixel-level mask constrained image synthesis. The output shape of their proposed model does not always align with the input mask.

2.2 State-of-the-art Pix2Pix Style Generator

Figure 2: Reachable sample space under different mapping mechanisms.

Many works pix2pix2016 ; Pix2pix_HD ; Thermal_pix2pix ; text_embedding ; Retinal_GAN have applied image translation models to map the image from one domain to another. However, a typical Pix2Pix setup does not perform well in terms of fine-grained texture details and feature diversity. The state of art Pix2Pix-HD model on the other hand proposes a coarse-to-fine model architecture design with perceptual loss and multi-scale discriminators. The main idea is to use additional loss terms to regularize the expanded model capacity, especially the concatenated refinement networks. Though the proposed model has a mechanism of randomizing the textures through instance level feature embedding, diversity of local texture details still relies on the minor perturbations of instance label maps. To some extent this mechanism allows stochastic texture generation, however, the mapping of textures is coupled only with the shape perturbation of objects. In other words, the image translation model is still limited to one-to-one mapping as shown in Fig 1, and the image quality and diversity is rather low due to this limitation.

2.3 Progressive Growing of GAN

Progressive growing of GAN(pGAN) karras2018progressive is a training methodology that gradually adds convolution layers to the generator and discriminator to achieve better stability and faster convergence. This technique makes it possible to synthesize high resolution images using a slightly modified DCGAN DCGAN generator and discriminator architecture. Several recent studies style_gan ; pGAN_Brain ; pgan_mammo have applied the progressive training strategy and achieved high resolution of synthesized results in non-conditional settings. We also apply progressive training strategy to achieve high resolution outputs.

3 Mask Embedding in Generator

To control the shape of generator output, a mask is typically used as the only input in an encoder-decoder style generator to enforce the pixel-level constraint. The fundamental principle of such image translation models is to build a translation of where one to one translations are established give input . With mechanism such as drop-out or noise overlaid denoted as to input , the one to more relation becomes theoretically possible in ideal cases. However, limited by the convolution operations and choice of objective function, Pix2Pix reported that overlaid noise is often ignored by the model. Model output typically depends heavily on the semantic input mask and drop-out so that the diversity of high frequency texture patterns is limited. In other words, given a sub optimal image-to-image translation network the sampling scheme in practice becomes where . The mapped sample space thus becomes sparse. As illustrated in Fig 2, we postulate that these different strategies (Pix2Pix, Pix2Pix-HD, and our proposed method) allow the model to sample increasingly larger subsets of the entire domain space, which in turns provides greater generator performance in terms of diversity, resolution and realism.

In this study we propose a new generator architecture design concept that maps a particular semantic input to the sample space more efficiently by coupling the latent input and the conditional input.

3.1 Pixel-Level Mask Constraint and Model Design

Figure 3: Architecture of our network. Left: a U-Net style generator. Right: a discriminator consists of several convolution layers.

Our proposed generator structure shown in Fig 3 is derived from the pGAN generator architecture karras2018progressive ; DCGAN , where the generator projects a latent vector onto the latent space following several up-sampling and convolution layers to form the output image. To inject the semantic information, we construct a series of mask features and concatenate them onto the corresponding latent features. This forms the U-Net style architecture that is similar to the one implemented in the Pix2Pix study but without the latent vector input. However, we observe this initial implementation output images with significantly reduced quality compared to the original pGAN framework. We regard this issue as a space sampling problem where the mask is posing a constraint on the feature projection path. The mapping of feature values from early layers to later layers becomes less reliable with the spatial and morphological constraints posed by the mask input, resulting in reduced model capacity and unstable training process. A reasonable solution is to implement a mechanism that allows the initial latent feature projections to be mostly coherent with the mask constraint. Then the model can use the short connections (horizontal arrows in Fig 3) of mask features only as a means to enforce the pixel-level constraint without consuming too much model capacity to refine global image structures. We achieve this mechanism by constructing a mask embedding vector and injecting it into the latent input vector, as shown in the bottom left of Fig 3.

3.2 Formulation

Figure 4: An illustrative example of generating an image of a dog using a dog mask as the guidance. Left: illustrative feature space of the image generation process using a series of convolution layers. Right: two examples of generating a dog image given a dog mask with and without mask embedding. For simplicity the latent features are visualized as low resolution images. At inference time, an ideal model with mask embedding projects base features onto the correct manifold and performs proper up-sampling through convolution layers; However, model without mask embedding learns to (1) project only average base image; (2) inefficiently map the average base image to a dog to comply with the mask constraint.

This section aims to give some intuition behind our structure. We regard having both mask constraint and local fine-grained texture details at the same time as a space sampling problem, under the condition that the up-sampling is mostly conducted with convolution layers. A mask input does not identify an image in the real image dataset, but instead relate to a cluster of real images. Hence masks gathered from dataset defines a partition of the real image manifolds. The top left ellipse in Fig 4 illustrates a two-partition of dog and cat masks. We also demonstrates the low resolution feature sets by smaller ellipses. Connected by a series of convolution layers, which is a local operation with limited receptive field, the partition is inherited hierarchically and admits similar geometry within each manifold. Our structure then aims to first correctly sample a mask constraint point in the lowest resolution manifold in two steps, i.e., (1) locate the correct partition via mask embedding and (2) sample a point within the partition via a latent feature vector. Then an up-sampling procedure refines the detail and enhance the mask constraint through the vertical injection of mask information as in Fig 3. Two components, latent feature vector and mask embedding, are the fundamental difference between our model and others. We would like to emphasize the importance of each of them.

Without latent feature vector, as in Pix2Pix or Pix2Pix-HD, the model with only mask input does not have sufficient randomness. Hence the generated image is nearly uniquely identified by the mask. The variety of the generated images through models without latent feature vector is very limited. In contrast, our model have latent feature vector, which encodes a large variety of details. Given a mask, we are able to generate dramatically different images still with fine details.

Without mask embedding, as in Tube-GAN, the constraint is less emphasized in the lower dimensional features and parameters in the later layers potentially have to correct incorrect low resolution image, which limits the capability in expressing details. While our model uses mask embedding which potentially finds the correct partition and generates correct latent space representation. Hence all later layers focus more on generating details. Fig 4 shows a cartoon of the comparison. The blue dash line indicates the process of our model and generates dog image at different resolutions in the second to the right column. Whereas the red dash line indicates the process without mask embedding. It generates a low resolution cat image in the beginning due to the lack of mask information. During the later layers, convolution together with mask injection correct the image from cat to dog. Unfortunately, the final image looks like a dog but is of much lower quality. Images in Fig 4 are not generated by models, but we do observe similar behavior in reality. These observations indicate that incorporating the mask embedding significantly improves the features projection efficiency.

3.3 Architecture

Our proposed model shown in Fig 3 consists of the mask projection path and the latent projection path corresponding to the contracting and expanding path in U-Net UNet

respectively. The input to the mask projection path is a binary face edge map. The mask undergoes a series of blocks, and each block consists of 2 convolution layers with strides of 1 and 2 respectively. Each block outputs an increasing number of features to the following layer and concatenate only the first 8 features to the latent projection path to form the mask constraint.

The mask projection path has two main functions. First it provides spatial constraint for the feature up-sampling process on the latent projection path. Second, it outputs the mask embedding that informs the latent projection layer the feature clusters that are most likely coherent with the particular mask. To reflect the fact that mask features from the left contracting path serves mainly as a constraint, only 8 mask features are concatenated to the latent projection path of the network. This design is based on two reasons: (1) more mask features require more capacity to properly merge them into projected latent features, increasing training difficulty; (2) our preliminary experiments indicate a trained model mostly project mask features with almost identical patterns merely in different numerical values.

4 Training

All three models are trained using the WGAN-GP loss formulationwgan_gp

. The Pix2Pix baseline was trained directly at target resolution for 25 epochs. Our proposed model with and without mask embedding was trained using the progressive growing strategy from the pGAN study

karras2018progressive . We start from an output resolution of , train for 45k steps and then fade in new convolution blocks that doubles the input and output resolution. Given the light-weightiness of the mask projection path, no fading connection is implemented.

To compare the effectiveness of mask embedding mechanism, the training schedule including batch size, learning rate and number of discriminator optimization per generator in this study is kept the same for our proposed models. We used a batch size of 256 at the output resolution of . We half the batch size every time when doubling the output resolution. The learning rate is initially set to constant at and increase to when the model reaches the output resolution of

. More details can be found in our source code. We use TensorFlow platform and each model in our experiment is trained on 4 NVIDIA V100 for 2 days to reach the final resolution of

.

5 Experiments

We compared the generators of Pix2Pix baseline (), our without embedding baseline (), and our proposed embedding model () on an image synthesis task using the CELEBA-HQ dataset. The pix2pix-HD model is not compared due to the fact that its instance-level feature embedding mechanism depends on the perturbation of masks to generate diverse images, while our proposed method focus on one to more mapping of the same semantic mask. Hyper-parameters of compared models are intentionally kept very similar at the latent projection path (up-sampling path for Pix2Pix) for controlled performance comparison. The discriminators of both our baseline and proposed model are identical containing parameters.Generator performance are measured using sliced Wasserstein distance(SWD).

5.1 CELEBA-HQ Dataset

The dataset we used to validate our approach is the CELEBA-HQ dataset originally compiled by celeba , later cleanup and augmented by karras2018progressive . We extracted 68 face landmarks for each face images in CELEBA-HQ dataset using the Dlib Python Librarydlib09 . The detection is performed at resolution of . We then constructed the face edge map simply by connecting the detected dots from each face landmark. Landmark detections significantly different from the original specified attributes celeba were removed. In total 27000 images were compiled as training images.

5.2 Quantitative Evaluation

We evaluated the effectiveness of proposed model using the sliced Wasserstein distance SWD SWD , following the parameter settings used previously karras2018progressive . Due to memory limitation, SWD was averaged over batches of real and synthesized images pairs. We first computed the SWD of 240 image pairs and then repeated until we cover 8192 pairs. We generated the Laplacian pyramid of the images from to the minimal resolution of . At each level of the pyramid we extracted 128 patches of size , normalized and computed the average distance for each level with respect the real images. In Table 1, the SWD metric captures the performance difference of our baseline and proposed models, as well as the Pix2Pix model. We can infer that using masking embedding is superior and improves the quality of synthesized images, which is also consistent with the visual observations.

Configurations 512 256 128 64 32 16 Avg
Real 10.82 9.98 10.14 9.75 9.83 7.52 9.67
Pix2Pix 67.74 27.72 25.08 20.46 19.05 151.78 65.52
Without Embedding 58.20 27.77 22.19 18.25 17.58 70.49 35.75
With Embedding 43.74 22.46 17.48 14.83 13.65 37.57 24.96
Table 1: Sliced Wasserstein distance(SWD) measured between the generated images of our baseline and proposed model to the training images. Each column is one level on the Laplacian pyramid.

5.3 Qualitative Comparison

(a) (b) (c) (d)
Figure 5: (a) Input mask. (b) Synthesized image using Pix2Pix (c) Synthesized image using our without-embedding baseline model. (d) Synthesized image using our proposed embedding model.

Fig 5 illustrates the synthesized results of the two baseline models and our proposed embedding model using the same mask as input. Compared to our proposed model, the Pix2Pix baseline is limited to generate coarse images in very similar style. For example, a particular model iteration during training generates only black or dark brown hair color, or the skin texture has the same waxy appearance. In this baseline Pix2Pix model, it is likely that the color and texture of faces are strongly coupled with the mask input, forcing the model to learn only the ’average’ face in the dataset, thus preventing the model from synthesizing diverse, fine-grained textures.

The model without embedding also failed to generate high fidelity textures. The generated images contain major noise realization and up-sampling artifacts that are indications of reduction in model capacity. This observation fits our hypothesis that the model without mask embedding is forced to project initial features onto space at the intersection of sample distributions, resulting in blurred texture patterns and ambiguous structures. As a consequence of insufficient generator capacity during training, the model also generates significantly more artifacts such as diagonal straight lines and checkerboard texture patterns.

5.4 Changing Latent Input

(a) (b) (c) (d) (e)
Figure 6: (a) Input mask. (b) Original Image. (c), (d), (e) synthesized images using the same mask but different latent vectors

We also demonstrate that the same mask input can be coupled with different latent vectors to form different faces in Fig 6. We noticed however that the latent vector and mask embedding were not completely disentangled. The latent vector is responsible more for the style of images, namely the hair style, skin color, facial hair, etc. On the other hand, the face landmarks are as expected determined by the provided mask. More results can be found in supplemental material. One limitation of this study is the small number of images compared to varieties of facial landmarks combinations generated. We observe some masks are coupled with specific characteristics such as gender and skin color that not necessarily obvious to human observer given a binary mask. This prevented the latent vector gaining control for better sample space mapping. For future work, this problem could potentially be alleviated using more abstract mask input together with a larger dataset. Moreover, implementing random blurring and mask feature drop output could potential help increase the output variety as well.

6 Conclusion

We have demonstrated the significance of mask embedding in high-resolution realistic image synthesis. Quantitative and qualitative evaluations validate the effectiveness of mask embedding mechanism. Our experiment is based on semantic input, and the same concept applies to other conditional input such as textures and text.

References

  • [1] Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. High-resolution image synthesis and semantic manipulation with conditional gans. In

    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

    , 2018.
  • [2] Qifeng Chen and Vladlen Koltun. Photographic image synthesis with cascaded refinement networks. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pages 1520–1529, 2017.
  • [3] Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris Metaxas. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In ICCV, 2017.
  • [4] Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial networks. In Doina Precup and Yee Whye Teh, editors,

    Proceedings of the 34th International Conference on Machine Learning

    , volume 70 of Proceedings of Machine Learning Research, pages 214–223, International Convention Centre, Sydney, Australia, 06–11 Aug 2017. PMLR.
  • [5] Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. Improved training of wasserstein gans. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 5767–5777. Curran Associates, Inc., 2017.
  • [6] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros.

    Image-to-image translation with conditional adversarial networks.

    arxiv, 2016.
  • [7] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, 2015.
  • [8] He Zhao, Huiqi Li, Sebastian Maurer-Stroh, and Li Cheng.

    Synthesizing retinal and neuronal images with generative adversarial nets.

    Medical Image Analysis, 49:14 – 26, 2018.
  • [9] Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. CoRR, abs/1411.1784, 2014.
  • [10] Jon Gauthier. Conditional generative adversarial nets for convolutional face generation. 2015.
  • [11] Xueping Wang, Weixin Li, Guodong Mu, Di Huang, and Yunhong Wang. Facial expression synthesis by u-net conditional generative adversarial networks. In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, ICMR ’18, pages 283–290, New York, NY, USA, 2018. ACM.
  • [12] G. Antipov, M. Baccouche, and J. Dugelay. Face aging with conditional generative adversarial networks. In 2017 IEEE International Conference on Image Processing (ICIP), pages 2089–2093, Sep. 2017.
  • [13] N. Bayramoglu, M. Kaakinen, L. Eklund, and J. Heikkilä. Towards virtual h e staining of hyperspectral lung histology images using conditional generative adversarial networks. In 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pages 64–71, Oct 2017.
  • [14] Mina Rezaei, Konstantin Harmuth, Willi Gierke, Thomas Kellermeier, Martin Fischer, Haojin Yang, and Christoph Meinel. A conditional adversarial network for semantic segmentation of brain tumor. In Alessandro Crimi, Spyridon Bakas, Hugo Kuijf, Bjoern Menze, and Mauricio Reyes, editors, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pages 241–252, Cham, 2018. Springer International Publishing.
  • [15] S. U. Dar, M. Yurt, L. Karacan, A. Erdem, E. Erdem, and T. Çukur. Image synthesis in multi-contrast mri with conditional generative adversarial networks. IEEE Transactions on Medical Imaging, pages 1–1, 2019.
  • [16] X. Liu, G. Meng, S. Xiang, and C. Pan. Semantic image synthesis via conditional cycle-generative adversarial networks. In 2018 24th International Conference on Pattern Recognition (ICPR), pages 988–993, Aug 2018.
  • [17] Gökhan Yildirim, Calvin Seward, and Urs Bergmann. Disentangling Multiple Conditional Inputs in GANs. arXiv e-prints, page arXiv:1806.07819, Jun 2018.
  • [18] Cunjian Chen and Arun Ross. Matching Thermal to Visible Face Images Using a Semantic-Guided Generative Adversarial Network. arXiv e-prints, page arXiv:1903.00963, Mar 2019.
  • [19] P. Costa, A. Galdran, M. I. Meyer, M. Niemeijer, M. Abràmoff, A. M. Mendonça, and A. Campilho. End-to-end adversarial retinal image synthesis. IEEE Transactions on Medical Imaging, 37(3):781–791, March 2018.
  • [20] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of GANs for improved quality, stability, and variation. In International Conference on Learning Representations, 2018.
  • [21] Joachim D. Curtó, Irene C. Zarza, Fernando De La Torre, Irwin King, and Michael R. Lyu. High-resolution deep convolutional generative adversarial networks, 2017. cite arxiv:1711.06491.
  • [22] Tamar Rott Shaham, Tali Dekel, and Tomer Michaeli. SinGAN: Learning a Generative Model from a Single Natural Image. arXiv e-prints, page arXiv:1905.01164, May 2019.
  • [23] Anthreas Antoniou, Amos Storkey, and Harrison Edwards. Data augmentation generative adversarial networks, 2018.
  • [24] Dimitrios Korkinof, Tobias Rijken, Michael O’Neill, Joseph Yearsley, Hugh Harvey, and Ben Glocker. High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks. arXiv e-prints, page arXiv:1807.03401, Jul 2018.
  • [25] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015.
  • [26] Davis E. King. Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research, 10:1755–1758, 2009.
  • [27] Rabin Julien, Gabriel Peyré, Julie Delon, and Bernot Marc. Wasserstein Barycenter and its Application to Texture Mixing. In SSVM’11, pages 435–446, Israel, 2011. Springer.