2022-07-21 更新
VoloGAN: Adversarial Domain Adaptation for Synthetic Depth Data
Authors:Sascha Kirch, Rafael Pagés, Sergio Arnaldo, Sergio Martín
We present VoloGAN, an adversarial domain adaptation network that translates synthetic RGB-D images of a high-quality 3D model of a person, into RGB-D images that could be generated with a consumer depth sensor. This system is especially useful to generate high amount training data for single-view 3D reconstruction algorithms replicating the real-world capture conditions, being able to imitate the style of different sensor types, for the same high-end 3D model database. The network uses a CycleGAN framework with a U-Net architecture for the generator and a discriminator inspired by SIV-GAN. We use different optimizers and learning rate schedules to train the generator and the discriminator. We further construct a loss function that considers image channels individually and, among other metrics, evaluates the structural similarity. We demonstrate that CycleGANs can be used to apply adversarial domain adaptation of synthetic 3D data to train a volumetric video generator model having only few training samples.
PDF
点此查看论文截图
Effect of Instance Normalization on Fine-Grained Control for Sketch-Based Face Image Generation
Authors:Zhihua Cheng, Xuejin Chen
Sketching is an intuitive and effective way for content creation. While significant progress has been made for photorealistic image generation by using generative adversarial networks, it remains challenging to take a fine-grained control on synthetic content. The instance normalization layer, which is widely adopted in existing image translation networks, washes away details in the input sketch and leads to loss of precise control on the desired shape of the generated face images. In this paper, we comprehensively investigate the effect of instance normalization on generating photorealistic face images from hand-drawn sketches. We first introduce a visualization approach to analyze the feature embedding for sketches with a group of specific changes. Based on the visual analysis, we modify the instance normalization layers in the baseline image translation model. We elaborate a new set of hand-drawn sketches with 11 categories of specially designed changes and conduct extensive experimental analysis. The results and user studies demonstrate that our method markedly improve the quality of synthesized images and the conformance with user intention.
PDF 9 pages, 12 figures
点此查看论文截图
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
Authors:Zijie Wu, Zhen Zhu, Junping Du, Xiang Bai
In this paper, we aim to devise a universally versatile style transfer method capable of performing artistic, photo-realistic, and video style transfer jointly, without seeing videos during training. Previous single-frame methods assume a strong constraint on the whole image to maintain temporal consistency, which could be violated in many cases. Instead, we make a mild and reasonable assumption that global inconsistency is dominated by local inconsistencies and devise a generic Contrastive Coherence Preserving Loss (CCPL) applied to local patches. CCPL can preserve the coherence of the content source during style transfer without degrading stylization. Moreover, it owns a neighbor-regulating mechanism, resulting in a vast reduction of local distortions and considerable visual quality improvement. Aside from its superior performance on versatile style transfer, it can be easily extended to other tasks, such as image-to-image translation. Besides, to better fuse content and style features, we propose Simple Covariance Transformation (SCT) to effectively align second-order statistics of the content feature with the style feature. Experiments demonstrate the effectiveness of the resulting model for versatile style transfer, when armed with CCPL.
PDF Accepted by ECCV2022 as an oral paper; code url: https://github.com/JarrentWu1031/CCPL Video demo: https://youtu.be/scZuJCXhL14
点此查看论文截图