41-Issue 7
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Item Real-Time Rendering of Eclipses without Incorporation of Atmospheric Effects(The Eurographics Association and John Wiley & Sons Ltd., 2022) Schneegans, Simon; Gilg, Jonas; Ahlers, Volker; Gerndt, Andreas; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneIn this paper, we present a novel approach for real-time rendering of soft eclipse shadows cast by spherical, atmosphereless bodies. While this problem may seem simple at first, it is complicated by several factors. First, the extreme scale differences and huge mutual distances of the involved celestial bodies cause rendering artifacts in practice. Second, the surface of the Sun does not emit light evenly in all directions (an effect which is known as limb darkening). This makes it impossible to model the Sun as a uniform spherical light source. Finally, our intended applications include real-time rendering of solar eclipses in virtual reality, which require very high frame rates. As a solution to these problems, we precompute the amount of shadowing into an eclipse shadow map, which is parametrized so that it is independent of the position and size of the occluder. Hence, a single shadow map can be used for all spherical occluders in the Solar System. We assess the errors introduced by various simplifications and compare multiple approaches in terms of performance and precision. Last but not least, we compare our approaches to the state-of-the-art and to reference images. The implementation has been published under the MIT license.Item Large-Scale Worst-Case Topology Optimization(The Eurographics Association and John Wiley & Sons Ltd., 2022) Zhang, Di; Zhai, Xiaoya; Fu, Xiao-Ming; Wang, Heming; Liu, Ligang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneWe propose a novel topology optimization method to efficiently minimize the maximum compliance for a high-resolution model bearing uncertain external loads. Central to this approach is a modified power method that can quickly compute the maximum eigenvalue to evaluate the worst-case compliance, enabling our method to be suitable for large-scale topology optimization. After obtaining the worst-case compliance, we use the adjoint variable method to perform the sensitivity analysis for updating the density variables. By iteratively computing the worst-case compliance, performing the sensitivity analysis, and updating the density variables, our algorithm achieves the optimized models with high efficiency. The capability and feasibility of our approach are demonstrated over various large-scale models. Typically, for a model of size 512×170×170 and 69934 loading nodes, our method took about 50 minutes on a desktop computer with an NVIDIA GTX 1080Ti graphics card with 11 GB memory.Item Local Offset Point Cloud Transformer Based Implicit Surface Reconstruction(The Eurographics Association and John Wiley & Sons Ltd., 2022) Yang, Yan Xin; Zhang, San Guo; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneImplicit neural representations, such as MLP, can well recover the topology of watertight object. However, MLP fails to recover geometric details of watertight object and complicated topology due to dealing with point cloud in a point-wise manner. In this paper, we propose a point cloud transformer called local offset point cloud transformer (LOPCT) as a feature fusion module. Before using MLP to learn the implicit function, the input point cloud is first fed into the local offset transformer, which adaptively learns the dependency of the local point cloud and obtains the enhanced features of each point. The feature-enhanced point cloud is then fed into the MLP to recover the geometric details and sharp features of watertight object and complex topology. Extensive reconstruction experiments of watertight object and complex topology demonstrate that our method achieves comparable or better results than others in terms of recovering sharp features and geometric details. In addition, experiments on watertight objects demonstrate the robustness of our method in terms of average result.Item Joint Hand and Object Pose Estimation from a Single RGB Image using High-level 2D Constraints(The Eurographics Association and John Wiley & Sons Ltd., 2022) Song, Hao-Xuan; Mu, Tai-Jiang; Martin, Ralph R.; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneJoint pose estimation of human hands and objects from a single RGB image is an important topic for AR/VR, robot manipulation, etc. It is common practice to determine both poses directly from the image; some recent methods attempt to improve the initial poses using a variety of contact-based approaches. However, few methods take the real physical constraints conveyed by the image into consideration, leading to less realistic results than the initial estimates. To overcome this problem, we make use of a set of high-level 2D features which can be directly extracted from the image in a new pipeline which combines contact approaches and these constraints during optimization. Our pipeline achieves better results than direct regression or contactbased optimization: they are closer to the ground truth and provide high quality contact.Item Resolution-switchable 3D Semantic Scene Completion(The Eurographics Association and John Wiley & Sons Ltd., 2022) Luo, Shoutong; Sun, Zhengxing; Sun, Yunhan; Wang, Yi; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneSemantic scene completion (SSC) aims to recover the complete geometric structure as well as the semantic segmentation results from partial observations. Previous works could only perform this task at a fixed resolution. To handle this problem, we propose a new method that can generate results at different resolutions without redesigning and retraining. The basic idea is to decouple the direct connection between resolution and network structure. To achieve this, we convert feature volume generated by SSC encoders into a resolution adaptive feature and decode this feature via point. We also design a resolution-adapted point sampling strategy for testing and a category-based point sampling strategy for training to further handle this problem. The encoder of our method can be replaced by existing SSC encoders. We can achieve better results at other resolutions while maintaining the same accuracy as the original resolution results. Code and data are available at https://github.com/lstcutong/ReS-SSC.Item Efficient and Stable Simulation of Inextensible Cosserat Rods by a Compact Representation(The Eurographics Association and John Wiley & Sons Ltd., 2022) Zhao, Chongyao; Lin, Jinkeng; Wang, Tianyu; Bao, Hujun; Huang, Jin; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtiennePiecewise linear inextensible Cosserat rods are usually represented by Cartesian coordinates of vertices and quaternions on the segments. Such representations use excessive degrees of freedom (DOFs), and need many additional constraints, which causes unnecessary numerical difficulties and computational burden for simulation. We propose a simple yet compact representation that exactly matches the intrinsic DOFs and naturally satisfies all such constraints. Specifically, viewing a rod as a chain of rigid segments, we encode its shape as the Cartesian coordinates of its root vertex, and use axis-angle representation for the material frame on each segment. Under our representation, the Hessian of the implicit time-stepping has special non-zero patterns. Exploiting such specialties, we can solve the associated linear equations in nearly linear complexity. Furthermore, we carefully designed a preconditioner, which is proved to be always symmetric positive-definite and accelerates the PCG solver in one or two orders of magnitude compared with the widely used block-diagonal one. Compared with other technical choices including Super-Helices, a specially designed compact representation for inextensible Cosserat rods, our method achieves better performance and stability, and can simulate an inextensible Cosserat rod with hundreds of vertices and tens of collisions in real time under relatively large time steps.Item A Wide Spectral Range Sky Radiance Model(The Eurographics Association and John Wiley & Sons Ltd., 2022) Vévoda, Petr; Bashford-Rogers, Tom; Kolářová, Monika; Wilkie, Alexander; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtiennePre-computed models of sky radiance are a tool to rapidly determine incident solar irradiance in applications as diverse as movie VFX, lighting simulation for architecture, experimental biology, and flight simulators. Several such models exist, but most provide data only for the visible range and, in some cases, for the near-UV. But for accurate simulations of photovoltaic plant yield and the thermal properties of buildings, a pre-computed reference sky model which covers the entire spectral range of terrestrial solar irradiance is needed: and this range is considerably larger than what extant models provide. We deliver this, and for a ground-based observer provide the three components of sky dome radiance, atmospheric transmittance, and polarisation. We also discuss the additional aspects that need to be taken into consideration when including the near-infrared in such a model. Additionally, we provide a simple standalone C++ implementation as well as an implementation with a GUI.Item A Drone Video Clip Dataset and its Applications in Automated Cinematography(The Eurographics Association and John Wiley & Sons Ltd., 2022) Ashtari, Amirsaman; Jung, Raehyuk; Li, Mingxiao; Noh, Junyong; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneDrones became popular video capturing tools. Drone videos in the wild are first captured and then edited by humans to contain aesthetically pleasing camera motions and scenes. Therefore, edited drone videos have extremely useful information for cinematography and for applications such as camera path planning to capture aesthetically pleasing shots. To design intelligent camera path planners, learning drone camera motions from these edited videos is essential. However, first, this requires to filter drone clips and extract their camera motions out of these edited videos that commonly contain both drone and non-drone content. Moreover, existing video search engines return the whole edited video as a semantic search result and cannot return only drone clips inside an edited video. To address this problem, we proposed the first approach that can automatically retrieve drone clips from an unlabeled video collection using high-level search queries, such as ''drone clips captured outdoor in daytime from rural places". The retrieved clips also contain camera motions, camera view, and 3D reconstruction of a scene that can help develop intelligent camera path planners. To train our approach, we needed numerous examples of edited drone videos. To this end, we introduced the first large-scale dataset composed of edited drone videos. This dataset is also used for training and validating our drone video filtering algorithm. Both quantitative and qualitative evaluations have confirmed the validity of our method.Item MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis(The Eurographics Association and John Wiley & Sons Ltd., 2022) Ren, Haocheng; Zhang, Hao; Zheng, Jia; Zheng, Jiaxiang; Tang, Rui; Huo, Yuchi; Bao, Hujun; Wang, Rui; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneWith the rapid development of data-driven techniques, data has played an essential role in various computer vision tasks. Many realistic and synthetic datasets have been proposed to address different problems. However, there are lots of unresolved challenges: (1) the creation of dataset is usually a tedious process with manual annotations, (2) most datasets are only designed for a single specific task, (3) the modification or randomization of the 3D scene is difficult, and (4) the release of commercial 3D data may encounter copyright issue. This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl Synthesis system, to facilitate the 3D scene modification and the 2D image synthesis for various vision tasks. In particular, we design a programmable pipeline with Domain-Specific Language, allowing users to select scenes from the commercial indoor scene database, synthesize scenes for different tasks with customized rules, and render various types of imagery data, such as color images, geometric structures, semantic labels. Our system eases the difficulty of customizing massive scenes for different tasks and relieves users from manipulating fine-grained scene configurations by providing user-controllable randomness using multilevel samplers. Most importantly, it empowers users to access commercial scene databases with millions of indoor scenes and protects the copyright of core data assets, e.g., 3D CAD models. We demonstrate the validity and flexibility of our system by using our synthesized data to improve the performance on different kinds of computer vision tasks. The project page is at https://coohom.github.io/MINERVAS.Item Fine-Grained Memory Profiling of GPGPU Kernels(The Eurographics Association and John Wiley & Sons Ltd., 2022) Buelow, Max von; Guthe, Stefan; Fellner, Dieter W.; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneMemory performance is a crucial bottleneck in many GPGPU applications, making optimizations for hardware and software mandatory. While hardware vendors already use highly efficient caching architectures, software engineers usually have to organize their data accordingly in order to efficiently make use of these, requiring deep knowledge of the actual hardware. In this paper we present a novel technique for fine-grained memory profiling that simulates the whole pipeline of memory flow and finally accumulates profiling values in a way that the user retains information about the potential region in the GPU program by showing these values separately for each allocation. Our memory simulator turns out to outperform state-of-theart memory models of NVIDIA architectures by a magnitude of 2.4 for the L1 cache and 1.3 for the L2 cache, in terms of accuracy. Additionally, we find our technique of fine grained memory profiling a useful tool for memory optimizations, which we successfully show in case of ray tracing and machine learning applications.Item Learning Dynamic 3D Geometry and Texture for Video Face Swapping(The Eurographics Association and John Wiley & Sons Ltd., 2022) Otto, Christopher; Naruniec, Jacek; Helminger, Leonhard; Etterlin, Thomas; Mignone, Graziana; Chandran, Prashanth; Zoss, Gaspard; Schroers, Christopher; Gross, Markus; Gotardo, Paulo; Bradley, Derek; Weber, Romann; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneFace swapping is the process of applying a source actor's appearance to a target actor's performance in a video. This is a challenging visual effect that has seen increasing demand in film and television production. Recent work has shown that datadriven methods based on deep learning can produce compelling effects at production quality in a fraction of the time required for a traditional 3D pipeline. However, the dominant approach operates only on 2D imagery without reference to the underlying facial geometry or texture, resulting in poor generalization under novel viewpoints and little artistic control. Methods that do incorporate geometry rely on pre-learned facial priors that do not adapt well to particular geometric features of the source and target faces. We approach the problem of face swapping from the perspective of learning simultaneous convolutional facial autoencoders for the source and target identities, using a shared encoder network with identity-specific decoders. The key novelty in our approach is that each decoder first lifts the latent code into a 3D representation, comprising a dynamic face texture and a deformable 3D face shape, before projecting this 3D face back onto the input image using a differentiable renderer. The coupled autoencoders are trained only on videos of the source and target identities, without requiring 3D supervision. By leveraging the learned 3D geometry and texture, our method achieves face swapping with higher quality than when using offthe- shelf monocular 3D face reconstruction, and overall lower FID score than state-of-the-art 2D methods. Furthermore, our 3D representation allows for efficient artistic control over the result, which can be hard to achieve with existing 2D approaches.Item NSTO: Neural Synthesizing Topology Optimization for Modulated Structure Generation(The Eurographics Association and John Wiley & Sons Ltd., 2022) Zhong, Shengze; Punpongsanon, Parinya; Iwai, Daisuke; Sato, Kosuke; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneNature evolves structures like honeycombs at optimized performance with limited material. These efficient structures can be artificially created with the collaboration of structural topology optimization and additive manufacturing. However, the extensive computation cost of topology optimization causes low mesh resolution, long solving time, and rough boundaries that fail to match the requirements for meeting the growing personal fabrication demands and printing capability. Therefore, we propose the neural synthesizing topology optimization that leverages a self-supervised coordinate-based network to optimize structures with significantly shorter computation time, where the network encodes the structural material layout as an implicit function of coordinates. Continuous solution space is further generated from optimization tasks under varying boundary conditions or constraints for users' instant inference of novel solutions. We demonstrate the system's efficacy for a broad usage scenario through numerical experiments and 3D printing.Item EL-GAN: Edge-Enhanced Generative Adversarial Network for Layout-to-Image Generation(The Eurographics Association and John Wiley & Sons Ltd., 2022) Gao, Lin; Wu, Lei; Meng, Xiangxu; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneAlthough some progress has been made in the layout-to-image generation of complex scenes with multiple objects, object-level generation still suffers from distortion and poor recognizability. We argue that this is caused by the lack of feature encodings for edge information during image generation. In order to solve these limitations, we propose a novel edge-enhanced Generative Adversarial Network for layout-to-image generation (termed EL-GAN). The feature encodings of edge information are learned from the multi-level features output by the generator and iteratively optimized along the generator's pipeline. Two new components are included at each generator level to enable multi-scale learning. Specifically, one is the edge generation module (EGM), which is responsible for converting the output of the multi-level features by the generator into images of different scales and extracting their edge maps. The other is the edge fusion module (EFM), which integrates the feature encodings refined from the edge maps into the subsequent image generation process by modulating the parameters in the normalization layers. Meanwhile, the discriminator is fed with frequency-sensitive image features, which greatly enhances the generation quality of the image's high-frequency edge contours and low-frequency regions. Extensive experiments show that EL-GAN outperforms the state-of-the-art methods on the COCO-Stuff and Visual Genome datasets. Our source code is available at https://github.com/Azure616/EL-GAN.Item Learning 3D Shape Aesthetics Globally and Locally(The Eurographics Association and John Wiley & Sons Ltd., 2022) Chen, Minchan; Lau, Manfred; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneThere exist previous works in computing the visual aesthetics of 3D shapes ''globally'', where the term global means that shape aesthetics data are collected for whole 3D shapes and then used to compute the aesthetics of whole 3D shapes. In this paper, we introduce a novel method that takes such ''global'' shape aesthetics data, and learn both a ''global'' shape aesthetics measure that computes aesthetics scores for whole 3D shapes, and a ''local'' shape aesthetics measure that computes to what extent a local region on the 3D shape surface contributes to the whole shape's aesthetics. These aesthetics measures are learned, and hence do not consider existing handcrafted notions of what makes a 3D shape aesthetic. We take a dataset of global pairwise shape aesthetics, where humans compares between pairs of shapes and say which shape from each pair is more aesthetic. Our solution proposes a point-based neural network that takes a 3D shape represented by surface patches as input and jointly outputs its global aesthetics score and a local aesthetics map. To build connections between global and local aesthetics, we embed the global and local features into the same latent space and then output scores with the weights-shared aesthetics predictors. Furthermore, we designed three loss functions to supervise the training jointly. We demonstrate the shape aesthetics results globally and locally to show that our framework can make good global aesthetics predictions while the predicted aesthetics maps are consistent with human perception. In addition, we present several applications enabled by our local aesthetics metric.Item ShadowPatch: Shadow Based Segmentation for Reliable Depth Discontinuities in Photometric Stereo(The Eurographics Association and John Wiley & Sons Ltd., 2022) Heep, Moritz; Zell, Eduard; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtiennePhotometric stereo is a well-established method with outstanding traits to recover surface details and material properties, like surface albedo or even specularity. However, while the surface is locally well-defined, computing absolute depth by integrating surface normals is notoriously difficult. Integration errors can be introduced and propagated by numerical inaccuracies from inter-reflection of light or non-Lambertian surfaces. But especially ignoring depth discontinuities for overlapping or disconnected objects, will introduce strong distortion artefacts. During the acquisition process the object is lit from different positions and self-shadowing is in general considered as an unavoidable drawback, complicating the numerical estimation of normals. However, we observe that shadow boundaries correlate strongly with depth discontinuities and exploit the visual structure introduced by self-shadowing to create a consistent image segmentation of continuous surfaces. In order to make depth estimation more robust, we deeply integrate photometric stereo with depth-from-stereo. Having obtained a shadow based segmentation of continuous surfaces, allows us to reduce the computational cost for correspondence search in depth-from-stereo. To speed-up computation further, we merge segments into larger meta-segments during an iterative depth optimization. The reconstruction error of our method is equal or smaller than previous work, and reconstruction results are characterized by robust handling of depth-discontinuities, without any smearing artifacts.Item Depth-Aware Shadow Removal(The Eurographics Association and John Wiley & Sons Ltd., 2022) Fu, Yanping; Gai, Zhenyu; Zhao, Haifeng; Zhang, Shaojie; Shan, Ying; Wu, Yang; Tang, Jin; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneShadow removal from a single image is an ill-posed problem because shadow generation is affected by the complex interactions of geometry, albedo, and illumination. Most recent deep learning-based methods try to directly estimate the mapping between the non-shadow and shadow image pairs to predict the shadow-free image. However, they are not very effective for shadow images with complex shadows or messy backgrounds. In this paper, we propose a novel end-to-end depth-aware shadow removal method without using depth images, which estimates depth information from RGB images and leverages the depth feature as guidance to enhance shadow removal and refinement. The proposed framework consists of three components, including depth prediction, shadow removal, and boundary refinement. First, the depth prediction module is used to predict the corresponding depth map of the input shadow image. Then, we propose a new generative adversarial network (GAN) method integrated with depth information to remove shadows in the RGB image. Finally, we propose an effective boundary refinement framework to alleviate the artifact around boundaries after shadow removal by depth cues. We conduct experiments on several public datasets and real-world shadow images. The experimental results demonstrate the efficiency of the proposed method and superior performance against state-of-the-art methods.Item Exploring Contextual Relationships in 3D Cloud Points by Semantic Knowledge Mining(The Eurographics Association and John Wiley & Sons Ltd., 2022) Chen, Lianggangxu; Lu, Jiale; Cai, Yiqing; Wang, Changbo; He, Gaoqi; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne3D scene graph generation (SGG) aims to predict the class of objects and predicates simultaneously in one 3D point cloud scene with instance segmentation. Since the underlying semantic of 3D point clouds is spatial information, recent ideas of the 3D SGG task usually face difficulties in understanding global contextual semantic relationships and neglect the intrinsic 3D visual structures. To build the global scope of semantic relationships, we first propose two types of Semantic Clue (SC) from entity level and path level, respectively. SC can be extracted from the training set and modeled as the co-occurrence probability between entities. Then a novel Semantic Clue aware Graph Convolution Network (SC-GCN) is designed to explicitly model each SC of which the message is passed in their specific neighbor pattern. For constructing the interactions between the 3D visual and semantic modalities, a visual-language transformer (VLT) module is proposed to jointly learn the correlation between 3D visual features and class label embeddings. Systematic experiments on the 3D semantic scene graph (3DSSG) dataset show that our full method achieves state-of-the-art performance.Item Point-augmented Bi-cubic Subdivision Surfaces(The Eurographics Association and John Wiley & Sons Ltd., 2022) Karciauskas, Kestutis; Peters, Jorg; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtiennePoint-Augmented Subdivision (PAS) replaces complex geometry-dependent guided subdivision, known to yield high-quality surfaces, by explicit subdivision formulas that yield similarly-good limit surfaces and are easy to implement using any subdivision infrastructure: map the control net d augmented by a fixed central limit point C, to a finer net (˜d;C) = M(d;C), where the subdivision matrix M is assembled from the provided stencil Tables. Point-augmented bi-cubic subdivision improves the state of the art so that bi-cubic subdivision surfaces can be used in high-end geometric design: the highlight line distribution for challenging configurations lacks the shape artifacts usually associated with explicit iterative generalized subdivision operators near extraordinary points. Five explicit formulas define Point-augmented bi-cubic subdivision in addition to uniform B-spline knot insertion. Point-augmented bi-cubic subdivision comes in two flavors, either generating a sequence of C2-joined surface rings (PAS2) or C1-joined rings (PAS1) that have fewer pieces.Item MeshFormer: High-resolution Mesh Segmentation with Graph Transformer(The Eurographics Association and John Wiley & Sons Ltd., 2022) Li, Yuan; He, Xiangyang; Jiang, Yankai; Liu, Huan; Tao, Yubo; Hai, Lin; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneGraph transformer has achieved remarkable success in graph-based segmentation tasks. Inspired by this success, we propose a novel method named MeshFormer for applying the graph transformer to the semantic segmentation of high-resolution meshes. The main challenges are the large data size, the massive model size, and the insufficient extraction of high-resolution semantic meanings. The large data or model size necessitates unacceptably extensive computational resources, and the insufficient semantic meanings lead to inaccurate segmentation results. MeshFormer addresses these three challenges with three components. First, a boundary-preserving simplification is introduced to reduce the data size while maintaining the critical high-resolution information in segmentation boundaries. Second, a Ricci flow-based clustering algorithm is presented for constructing hierarchical structures of meshes, replacing many convolutions layers for global support with only a few convolutions in hierarchy structures. In this way, the model size can be reduced to an acceptable range. Third, we design a graph transformer with cross-resolution convolutions, which extracts richer high-resolution semantic meanings and improves segmentation results over previous methods. Experiments show that MeshFormer achieves gains from 1.0% to 5.8% on artificial and real-world datasets.Item Real-Time Video Deblurring via Lightweight Motion Compensation(The Eurographics Association and John Wiley & Sons Ltd., 2022) Son, Hyeongseok; Lee, Junyong; Cho, Sunghyun; Lee, Seungyong; Umetani, Nobuyuki; Wojtan, Chris; Vouga, EtienneWhile motion compensation greatly improves video deblurring quality, separately performing motion compensation and video deblurring demands huge computational overhead. This paper proposes a real-time video deblurring framework consisting of a lightweight multi-task unit that supports both video deblurring and motion compensation in an efficient way. The multi-task unit is specifically designed to handle large portions of the two tasks using a single shared network and consists of a multi-task detail network and simple networks for deblurring and motion compensation. The multi-task unit minimizes the cost of incorporating motion compensation into video deblurring and enables real-time deblurring. Moreover, by stacking multiple multi-task units, our framework provides flexible control between the cost and deblurring quality. We experimentally validate the state-of-theart deblurring quality of our approach, which runs at a much faster speed compared to previous methods and show practical real-time performance (30.99dB@30fps measured on the DVD dataset).
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