3DIAS: 3D Shape Reconstruction with Implicit Algebraic Surfaces

3D Shape representation has substantial effects on 3D shape reconstruction. Primitive-based representations approximate a 3D shape mainly by a set of simple implicit primitives, but the low geometrical complexity of the primitives limits the shape resolution. Moreover, setting a sufficient number of primitives for an arbitrary shape is challenging. To overcome these issues, we propose a constrained implicit algebraic surface as the primitive with few learnable coefficients and higher geometrical complexities and a deep neural network to produce these primitives. Our experiments demonstrate the superiorities of our method in terms of representation power compared to the state-of-the-art methods in single RGB image 3D shape reconstruction. Furthermore, we show that our method can semantically learn segments of 3D shapes in an unsupervised manner. The code is publicly available from https://myavartanoo.github.io/3dias/ .

Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report

Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services. While many solutions have been proposed for this task, the majority of them are too computationally expensive to run on portable devices with limited hardware resources. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based video super-resolution solutions that can achieve a real-time performance on mobile GPUs. The participants were provided with the REDS dataset and trained their models to do an efficient 4X video upscaling. The runtime of all models was evaluated on the OPPO Find X2 smartphone with the Snapdragon 865 SoC capable of accelerating floating-point networks on its Adreno GPU. The proposed solutions are fully compatible with any mobile GPU and can upscale videos to HD resolution at up to 80 FPS while demonstrating high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

NTIRE 2021 Challenge on Video Super-Resolution

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks.

NTIRE 2021 Challenge on Image Deblurring

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts.

Clean Images are Hard to Reblur: A New Clue for Deblurring

The goal of dynamic scene deblurring is to remove the motion blur present in a given image. Most learning-based approaches implement their solutions by minimizing the L1 or L2 distance between the output and reference sharp image. Recent attempts improve the perceptual quality of the deblurred image by using features learned from visual recognition tasks. However, those features are originally designed to capture the high-level contexts rather than the low-level structures of the given image, such as blurriness. We propose a novel low-level perceptual loss to make image sharper. To better focus on image blurriness, we train a reblurring module amplifying the unremoved motion blur. Motivated that a well-deblurred clean image should contain zero-magnitude motion blur that is hard to be amplified, we design two types of reblurring loss functions. The supervised reblurring loss at training stage compares the amplified blur between the deblurred image and the reference sharp image. The self-supervised reblurring loss at inference stage inspects if the deblurred image still contains noticeable blur to be amplified. Our experimental results demonstrate the proposed reblurring losses improve the perceptual quality of the deblurred images in terms of NIQE and LPIPS scores as well as visual sharpness.

SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation

Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution (SR). However, conventional methods still resort to some predetermined integer scaling factors, e.g., x2 or x4. Thus, they are difficult to be applied when arbitrary target resolutions are required. Recent approaches extend the scope to real-valued upsampling factors, even with varying aspect ratios to handle the limitation. In this paper, we propose the SRWarp framework to further generalize the SR tasks toward an arbitrary image transformation. We interpret the traditional image warping task, specifically when the input is enlarged, as a spatially-varying SR problem. We also propose several novel formulations, including the adaptive warping layer and multiscale blending, to reconstruct visually favorable results in the transformation process. Compared with previous methods, we do not constrain the SR model on a regular grid but allow numerous possible deformations for flexible and diverse image editing. Extensive experiments and ablation studies justify the necessity and demonstrate the advantage of the proposed SRWarp method under various transformations.

3DCrowdNet: 2D Human Pose-Guided3D Crowd Human Pose and Shape Estimation in the Wild

Recovering accurate 3D human pose and shape from in-the-wild crowd scenes is highly challenging and barely studied, despite their common presence. In this regard, we present 3DCrowdNet, a 2D human pose-guided 3D crowd pose and shape estimation system for in-the-wild scenes. 2D human pose estimation methods provide relatively robust outputs on crowd scenes than 3D human pose estimation methods, as they can exploit in-the-wild multi-person 2D datasets that include crowd scenes. On the other hand, the 3D methods leverage 3D datasets, of which images mostly contain a single actor without a crowd. The train data difference impedes the 3D methods' ability to focus on a target person in in-the-wild crowd scenes. Thus, we design our system to leverage the robust 2D pose outputs from off-the-shelf 2D pose estimators, which guide a network to focus on a target person and provide essential human articulation information. We show that our 3DCrowdNet outperforms previous methods on in-the-wild crowd scenes. We will release the codes.

DAQ: Distribution-Aware Quantization for Deep Image Super-Resolution Networks

Quantizing deep convolutional neural networks for image super-resolution substantially reduces their computational costs. However, existing works either suffer from a severe performance drop in ultra-low precision of 4 or lower bit-widths, or require a heavy fine-tuning process to recover the performance. To our knowledge, this vulnerability to low precisions relies on two statistical observations of feature map values. First, distribution of feature map values varies significantly per channel and per input image. Second, feature maps have outliers that can dominate the quantization error. Based on these observations, we propose a novel distribution-aware quantization scheme (DAQ) which facilitates accurate training-free quantization in ultra-low precision. A simple function of DAQ determines dynamic range of feature maps and weights with low computational burden. Furthermore, our method enables mixed-precision quantization by calculating the relative sensitivity of each channel, without any training process involved. Nonetheless, quantization-aware training is also applicable for auxiliary performance gain. Our new method outperforms recent training-free and even training-based quantization methods to the state-of-the-art image super-resolution networks in ultra-low precision.

Searching for Controllable Image Restoration Networks

Diverse user preferences over images have recently led to a great amount of interest in controlling the imagery effects for image restoration tasks. However, existing methods require separate inference through the entire network per each output, which hinders users from readily comparing multiple imagery effects due to long latency. To this end, we propose a novel framework based on a neural architecture search technique that enables efficient generation of multiple imagery effects via two stages of pruning: task-agnostic and task-specific pruning. Specifically, task-specific pruning learns to adaptively remove the irrelevant network parameters for each task, while task-agnostic pruning learns to find an efficient architecture by sharing the early layers of the network across different tasks. Since the shared layers allow for feature reuse, only a single inference of the task-agnostic layers is needed to generate multiple imagery effects from the input image. Using the proposed task-agnostic and task-specific pruning schemes together significantly reduces the FLOPs and the actual latency of inference compared to the baseline. We reduce 95.7% of the FLOPs when generating 27 imagery effects, and make the GPU latency 73.0% faster on 4K-resolution images.

NeuralAnnot: Neural Annotator for in-the-wild Expressive 3D Human Pose and Mesh Training Sets

Recovering expressive 3D human pose and mesh from in-the-wild images is greatly challenging due to the absence of the training data. Several optimization-based methods have been used to obtain pseudo-groundtruth (GT) 3D poses and meshes from GT 2D poses. However, they often produce bad ones with long running time because their frameworks are optimized on each sample only using 2D supervisions in a sequential way. To overcome the limitations, we present NeuralAnnot, a neural annotator that learns to construct in-the-wild expressive 3D human pose and mesh training sets. Our NeuralAnnot is trained on a large number of samples by 2D supervisions from a target in-the-wild dataset and 3D supervisions from auxiliary datasets with GT 3D poses in a parallel way. We show that our NeuralAnnot produces far better 3D pseudo-GTs with much shorter running time than the optimization-based methods, and the newly obtained training set brings great performance gain. The newly obtained training sets and codes will be publicly available.