In this paper, we study the task of hallucinating an authentic high-resolution (HR) face from an occluded thumbnail. We propose a multi-stage Progressive Upsampling and Inpainting Generative Adversarial Network, dubbed Pro-UIGAN, which exploits facial geometry priors to replenish and upsample (8*) the occluded and tiny faces (16*16 pixels). Pro-UIGAN iteratively (1) estimates facial geometry priors for low-resolution (LR) faces and (2) acquires non-occluded HR face images under the guidance of the estimated priors. Our multi-stage hallucination network super-resolves and inpaints occluded LR faces in a coarse-to-fine manner, thus reducing unwanted blurriness and artifacts significantly. Specifically, we design a novel cross-modal transformer module for facial priors estimation, in which an input face and its landmark features are formulated as queries and keys, respectively. Such a design encourages joint feature learning across the input facial and landmark features, and deep feature correspondences will be discovered by attention. Thus, facial appearance features and facial geometry priors are learned in a mutual promotion manner. Extensive experiments demonstrate that our Pro-UIGAN achieves visually pleasing HR faces, reaching superior performance in downstream tasks, i.e., face alignment, face parsing, face recognition and expression classification, compared with other state-of-the-art (SotA) methods.
Image-based virtual try-on is challenging in fitting a target in-shop clothes into a reference person under diverse human poses. Previous works focus on preserving clothing details ( e.g., texture, logos, patterns ) when transferring desired clothes onto a target person under a fixed pose. However, the performances of existing methods significantly dropped when extending existing methods to multi-pose virtual try-on. In this paper, we propose an end-to-end Semantic Prediction Guidance multi-pose Virtual Try-On Network (SPG-VTON), which could fit the desired clothing into a reference person under arbitrary poses. Concretely, SPG-VTON is composed of three sub-modules. First, a Semantic Prediction Module (SPM) generates the desired semantic map. The predicted semantic map provides more abundant guidance to locate the desired clothes region and produce a coarse try-on image. Second, a Clothes Warping Module (CWM) warps in-shop clothes to the desired shape according to the predicted semantic map and the desired pose. Specifically, we introduce a conductible cycle consistency loss to alleviate the misalignment in the clothes warping process. Third, a Try-on Synthesis Module (TSM) combines the coarse result and the warped clothes to generate the final virtual try-on image, preserving details of the desired clothes and under the desired pose. Besides, we introduce a face identity loss to refine the facial appearance and maintain the identity of the final virtual try-on result at the same time. We evaluate the proposed method on the most massive multi-pose dataset (MPV) and the DeepFashion dataset. The qualitative and quantitative experiments show that SPG-VTON is superior to the state-of-the-art methods and is robust to the data noise, including background and accessory changes, i.e., hats and handbags, showing good scalability to the real-world scenario.
In this paper, we propose to utilize Automated Machine Learning to automatically search architecture for deepfake detection. Unlike previous works, our method benefits from the superior capability of deep learning while relieving us from the high labor cost in the manual network design process. It is experimentally proved that our proposed method not only outperforms previous non-deep learning methods but achieves comparable or even better prediction accuracy compared to previous deep learning methods. To improve the generality of our method, especially when training data and testing data are manipulated by different methods, we propose a multi-task strategy in our network learning process, making it estimate potential manipulation regions in given samples as well as predict whether the samples are real. Comparing to previous works using similar strategies, our method depends much less on prior knowledge, such as no need to know which manipulation method is utilized and whether it is utilized already. Extensive experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method on deepfake detection.
Convolutional neural networks may perform poorly when the test and train data are from different domains. While this problem can be mitigated by using the target domain data to align the source and target domain feature representations, the target domain data may be unavailable due to privacy concerns. Consequently, there is a need for methods that generalize well without access to target domain data during training. In this work, we propose an adversarial hallucination approach, which combines a class-wise hallucination module and a semantic segmentation module. Since the segmentation performance varies across different classes, we design a semantic-conditioned style hallucination layer to adaptively stylize each class. The classwise stylization parameters are generated from the semantic knowledge in the segmentation probability maps of the source domain image. Both modules compete adversarially, with the hallucination module generating increasingly 'difficult' style images to challenge the segmentation module. In response, the segmentation module improves its performance as it is trained with generated samples at an appropriate class-wise difficulty level. Experiments on state of the art domain adaptation work demonstrate the efficacy of our proposed method when no target domain data are available for training.
Resolving a linear combination of point sources from their band-limited Fourier data is a fundamental problem in imaging and signal processing. With the incomplete Fourier data and the inevitable noise in the measurement, there is a fundamental limit on the separation distance between point sources that can be resolved. This is the so-called resolution limit problem. Characterization of this resolution limit is still a long-standing puzzle despite the prevalent use of the classic Rayleigh limit. It is well-known that Rayleigh limit is heuristic and its drawbacks become prominent when dealing with data that is subjected to delicate processing, as is what modern computational imaging methods do. Therefore, more precise characterization of the resolution limit becomes increasingly necessary with the development of data processing methods. For this purpose, we developed a theory of "computational resolution limit" for both number detection and support recovery in one dimension in [arXiv:2003.02917[cs.IT], arXiv:1912.05430[eess.IV]]. In this paper, we extend the one-dimensional theory to multi-dimensions. More precisely, we define and quantitatively characterize the "computational resolution limit" for the number detection and support recovery problems in a general k-dimensional space. Our results indicate that there exists a phase transition phenomenon regarding to the super-resolution factor and the signal-to-noise ratio in each of the two recovery problems. Our main results are derived using a subspace projection strategy. Finally, to verify the theory, we proposed deterministic subspace projection based algorithms for the number detection and support recovery problems in dimension two and three. The numerical results confirm the phase transition phenomenon predicted by the theory.
In this paper, we revisit the Image-to-Image (I2I) translation problem with transition consistency, namely the consistency defined on the conditional data mapping between each data pairs. Explicitly parameterizing each data mappings with a transition variable $t$, i.e., $x \overset{t(x,y)}{\mapsto}y$, we discover that existing I2I translation models mainly focus on maintaining consistency on results, e.g., image reconstruction or attribute prediction, named result consistency in our paper. This restricts their generalization ability to generate satisfactory results with unseen transitions in the test phase. Consequently, we propose to enforce both result consistency and transition consistency for I2I translation, to benefit the problem with a closer consistency between the input and output. To benefit the generalization ability of the translation model, we propose transition encoding to facilitate explicit regularization of these two {kinds} of consistencies on unseen transitions. We further generalize such explicitly regularized consistencies to distribution-level, thus facilitating a generalized overall consistency for I2I translation problems. With the above design, our proposed model, named Transition Encoding GAN (TEGAN), can poss superb generalization ability to generate realistic and semantically consistent translation results with unseen transitions in the test phase. It also provides a unified understanding of the existing GAN-based I2I transition models with our explicitly modeling of the data mapping, i.e., transition. Experiments on four different I2I translation tasks demonstrate the efficacy and generality of TEGAN.
We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging. Previous solutions have sought to explicitly recover the 3D geometry (e.g., as point clouds) or voxel density (e.g., within a pre-defined volume) of the hidden scene. In contrast, inspired by the recent Neural Radiance Field (NeRF) approach, we use a multi-layer perceptron (MLP) to represent the neural transient field or NeTF. However, NeTF measures the transient over spherical wavefronts rather than the radiance along lines. We therefore formulate a spherical volume NeTF reconstruction pipeline, applicable to both confocal and non-confocal setups. Compared with NeRF, NeTF samples a much sparser set of viewpoints (scanning spots) and the sampling is highly uneven. We thus introduce a Monte Carlo technique to improve the robustness in the reconstruction. Comprehensive experiments on synthetic and real datasets demonstrate NeTF provides higher quality reconstruction and preserves fine details largely missing in the state-of-the-art.
In this paper, the Point Adversarial Self Mining (PASM) approach, a simple yet effective way to progressively mine knowledge from training samples, is proposed to produce training data for CNNs to improve the performance and network generality in Facial Expression Recognition (FER) task. In order to achieve a high prediction accuracy under real-world scenarios, most of the existing works choose to manipulate the network architectures and design sophisticated loss terms. Although demonstrated to be effective in real scenarios, those aforementioned methods require extra efforts in network design. Inspired by random erasing and adversarial erasing, we propose PASM for data augmentation, simulating the data distribution in the wild. Specifically, given a sample and a pre-trained network, our proposed approach locates the informative region in the sample generated by point adversarial attack policy. The informative region is highly structured and sparse. Comparing to the regions produced by random erasing which selects the region in a purely random way and adversarial erasing which operates by attention maps, the located informative regions obtained by PASM are more adaptive and better aligned with the previous findings: not all but only a few facial regions contribute to the accurate prediction. Then, the located informative regions are masked out from the original samples to generate augmented images, which would force the network to explore additional information from other less informative regions. The augmented images are used to finetune the network to enhance its generality. In the refinement process, we take advantage of knowledge distillation, utilizing the pre-trained network to provide guidance and retain knowledge from old samples to train a new network with the same structural configuration.