Space-time video super-resolution (STVSR) aims to increase the spatial and temporal resolutions of low-resolution and low-frame-rate videos. Recently, deformable convolution based methods have achieved promising STVSR performance, but they could only infer the intermediate frame pre-defined in the training stage. Besides, these methods undervalued the short-term motion cues among adjacent frames. In this paper, we propose a Temporal Modulation Network (TMNet) to interpolate arbitrary intermediate frame(s) with accurate high-resolution reconstruction. Specifically, we propose a Temporal Modulation Block (TMB) to modulate deformable convolution kernels for controllable feature interpolation. To well exploit the temporal information, we propose a Locally-temporal Feature Comparison (LFC) module, along with the Bi-directional Deformable ConvLSTM, to extract short-term and long-term motion cues in videos. Experiments on three benchmark datasets demonstrate that our TMNet outperforms previous STVSR methods. The code is available at https://github.com/CS-GangXu/TMNet.
ByteDance Inc（字节跳动公司）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shanghai AI Laboratory（上海人工智能实验室）
Face image super resolution (face hallucination) usually relies on facial priors to restore realistic details and preserve identity information. Recent advances can achieve impressive results with the help of GAN prior. They either design complicated modules to modify the fixed GAN prior or adopt complex training strategies to finetune the generator. In this work, we propose a generative and controllable face SR framework, called GCFSR, which can reconstruct images with faithful identity information without any additional priors. Generally, GCFSR has an encoder-generator architecture. Two modu…
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）
Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient. However, real images always contain multiple types of degradation, which cannot be well determined by one coefficient. To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. Compared with the previous single-dimension (SD) modulation, the MD is …
Towards Controllable Low-Light Image Enhancement: A Continuous Multi-illumination Dataset and Efficient State Space Framework
Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；School of Automation, China University of Geosciences（中国地质大学自动化学院）；Department of Electromechanical Engineering, University of Macau（澳门大学机电工程系）
The University of Tokyo（东京大学）；RIKEN AIP（理化学研究所先进智能项目中心）；National University of Defense Technology（国防科技大学）；Hitachi, Ltd（日立制作所）
Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring paired training data, but is challenging because only low-resolution (LR) source and high-resolution (HR) guide images from different modalities are available. Existing methods utilize pseudo or weak supervision in LR space and thus deliver results that are blurry or not faithful to the source modality. To address this issue, we present a mutual modulation SR (MMSR) model, which tackles the task by a mutual modulation strategy, including a source-to-guide modulation and a guide-to-source modulation. In t…
Modulating image restoration level aims to generate a restored image by altering a factor that represents the restoration strength. Previous works mainly focused on optimizing the mean squared reconstruction error, which brings high reconstruction accuracy but lacks finer texture details. This paper presents a Controllable Unet Generative Adversarial Network (CUGAN) to generate high-frequency textures in the modulation tasks. CUGAN consists of two modules -- base networks and condition networks. The base networks comprise a generator and a discriminator. In the generator, we realize the intera…
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Peng Cheng Laboratory（鹏城实验室）；Tencent PCG（腾讯平台与内容事业群）；Shanghai AI Laboratory（上海人工智能实验室）
Interactive image restoration aims to restore images by adjusting several controlling coefficients, which determine the restoration strength. Existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. They usually suffer from a severe performance drop when the real degradation is different from their assumptions. Such a limitation is due to the complexity of real-world degradations, which can not provide explicit supervision to the interactive modulation during training. However, how to realize the interactive modulation …
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inversion of real images and determination of controllable latent directions are computationally expensive operations. Moreover, the determination of controllable latent directions requires additional human supervision. This work aims to explore the efficacy of mask-guided feature modulation in the laten…
Geometry-guided Emotion Modulation for Controllable and Photorealistic Emotional Talking Face Generation
School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）
Dynamic Frequency Modulation for Controllable Text-driven Image Generation
School of Geosciences and Info-Physics of Central South University（中南大学地球科学与信息物理学院）；School of Geography and Remote Sensing（地理科学与遥感学院）；Electronic Information School（电子信息学院）；School of Robotics（机器人学院）
Condition Weaving Meets Expert Modulation: Towards Universal and Controllable Image Generation
State Key Laboratory of Advanced Rail Autonomous Operation（先进轨道交通自主运行国家重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）；Visual Intellgence +X International Cooperation Joint Laboratory of MOE（教育部视觉智能+X国际合作联合实验室）；Hong Kong University of Science and Technology（香港科技大学）；SenseTime Research（商汤科技研究院）；Beijing Jiaotong University（北京交通大学）；SenseTime Research Institute（时光机器研究院）
Training-Free Multi-Style Fusion Through Reference-Based Adaptive Modulation
University of Washington, Seattle, WA 98195, USA（华盛顿大学）；Nanjing University of Posts and Telecommunications, Jiangsu, Nanjing, China（南京邮电大学）；Nanjing Foreign Language School, Jiangsu, Nanjing, China（南京外国语学校）
