GenAI4ELab/papercli-papers huggingface dataset:
Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization
Authors: Jingtang Liang; Xiaodong Cun; Chi-Man Pun; Jue Wang
Venue: ECCV 2022 (main)

Abstract: "Image harmonization aims to modify the color of the composited region according to the specific background. Previous works model this task as a pixel-wise image translation using UNet family structures. However, the model size and computational cost limit the ability of their models on edge devices and higher-resolution images. In this paper, we propose a spatial-separated curve rendering network (S2CRNet), a novel framework to prove that the simple global editing can effectively address this task as well as the challenge of high-resolution image harmonization for the first time. In S2CRNet, we design a curve rendering module (CRM) using spatial-specific knowledge to generate the parameters of the piece-wise curve mapping in the foreground region and we can directly render the original high-resolution images using the learned color curve. Besides, we also make two extensions of the proposed framework via cascaded refinement and semantic guidance. Experiments show that the proposed method reduces more than 90% of parameters compared with previous methods but still achieves the state-of-the-art performance on 3 benchmark datasets. Moreover, our method can work smoothly on higher resolution images with much lower GPU computational resources. The source codes are available at: \url{http://github.com/stefanLeong/S2CRNet}."
BigColor: Colorization Using a Generative Color Prior for Natural Images
Authors: Geonung Kim; Kyoungkook Kang; Seongtae Kim; Hwayoon Lee; Sehoon Kim; Jonghyun Kim; Seung-Hwan Baek; Sunghyun Cho
Venue: ECCV 2022 (main)

Abstract: "For realistic and vivid colorization, generative priors have recently been exploited. However, such generative priors often fail for in-the-wild complex images due to their limited representation space. In this paper, we propose BigColor, a novel colorization approach that provides vivid colorization for diverse in-the-wild images with complex structures. While previous generative priors are trained to synthesize both image structures and colors, we learn a generative color prior to focus on color synthesis given the spatial structure of an image. In this way, we reduce the burden of synthesizing image structures from the generative prior and expand its representation space to cover diverse images. To this end, we propose a BigGAN-inspired encoder-generator network that uses a spatial feature map instead of a spatially-flattened BigGAN latent code, resulting in an enlarged representation space. Our method enables robust colorization for diverse inputs in a single forward pass, supports arbitrary input resolutions, and provides multi-modal colorization results. We demonstrate that BigColor significantly outperforms existing methods especially on in-the-wild images with complex structures."
CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution
Authors: Cheeun Hong; Sungyong Baik; Heewon Kim; Seungjun Nah; Kyoung Mu Lee
Venue: ECCV 2022 (main)

Abstract: "Despite breakthrough advances in image super-resolution (SR) with convolutional neural networks (CNNs), SR has yet to enjoy ubiquitous applications due to the high computational complexity of SR networks. Quantization is one of the promising approaches to solve this problem. However, existing methods fail to quantize SR models with a bit-width lower than 8 bits, suffering from severe accuracy loss due to fixed bit-width quantization applied everywhere. In this work, to achieve high average bit-reduction with less accuracy loss, we propose a novel Content-Aware Dynamic Quantization (CADyQ) method for SR networks that allocates optimal bits to local regions and layers adaptively based on the local contents of an input image. To this end, a trainable bit selector module is introduced to determine the proper bit-width and quantization level for each layer and a given local image patch. This module is governed by the quantization sensitivity that is estimated by using both the average magnitude of image gradient of the patch and the standard deviation of the input feature of the layer. The proposed quantization pipeline has been tested on various SR networks and evaluated on several standard benchmarks extensively. Significant reduction in computational complexity and the elevated restoration accuracy clearly demonstrate the effectiveness of the proposed CADyQ framework for SR. Codes are available at https://github.com/Cheeun/CADyQ."
Deep Semantic Statistics Matching (D2SM) Denoising Network
Authors: Kangfu Mei; Vishal M. Patel; Rui Huang
Venue: ECCV 2022 (main)

Abstract: "The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains. But the optimization of end-to-end denoising learning like pixel-wise losses is performed in a sample-to-sample manner, which ignores the intrinsic correlation of images, especially semantics. In this paper, we introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network. It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space. By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks, and the denoised results can be better understood by high-level vision tasks. Comprehensive experiments conducted on the noisy Cityscapes dataset demonstrate the superiority of our method on both the denoising performance and semantic segmentation accuracy. Moreover, the performance improvement observed on our extended tasks including super-resolution and dehazing experiments shows its potentiality as a new general plug-and-play component."
3D Scene Inference from Transient Histograms
Authors: Sacha Jungerman; Atul Ingle; Yin Li; Mohit Gupta
Venue: ECCV 2022 (main)

Abstract: "Time-resolved image sensors that capture light at pico-to-nanosecond timescales were once limited to niche applications but are now rapidly becoming mainstream in consumer devices. We propose low-cost and low-power imaging modalities that capture scene information from minimal time-resolved image sensors with as few as one pixel. The key idea is to flood illuminate large scene patches (or the entire scene) with a pulsed light source and measure the time-resolved reflected light by integrating over the entire illuminated area. The one-dimensional measured temporal waveform, called transient, encodes both distances and albedoes at all visible scene points and as such is an aggregate proxy for the scene’s 3D geometry. We explore the viability and limitations of the transient waveforms for recovering scene information by itself, and also when combined with traditional RGB cameras. We show that plane estimation can be performed from a single transient and that using only a few more it is possible to recover a depth map of the whole scene. We also show two proof-of-concept hardware prototypes that demonstrate the feasibility of our approach for compact, mobile, and budget-limited applications."
Neural Space-Filling Curves
Authors: Hanyu Wang; Kamal Gupta; Larry Davis; Abhinav Shrivastava
Venue: ECCV 2022 (main)

Abstract: "We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images. Linear ordering of pixels forms the basis for many applications such as video scrambling, compression, and auto-regressive models that are used in generative modeling for images. Existing algorithms resort to a fixed scanning algorithm such as Raster scan or Hilbert scan. Instead, our work learns a spatially coherent linear ordering of pixels from the dataset of images using a graph-based neural network. The resulting Neural SFC is optimized for an objective suitable for the downstream task when the image is traversed along with the scan line order. We show the advantage of using Neural SFCs in downstream applications such as image compression. Code available in the supplementary material."
Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging
Authors: An Gia Vien; Chul Lee
Venue: ECCV 2022 (main)

Abstract: "We propose a novel single-shot high dynamic range (HDR) imaging algorithm based on exposure-aware dynamic weighted learning, which reconstructs an HDR image from a spatially varying exposure (SVE) raw image. First, we recover poorly exposed pixels by developing a network that learns local dynamic filters to exploit local neighboring pixels across color channels. Second, we develop another network that combines only valid features in well-exposed regions by learning exposure-aware feature fusion. Third, we synthesize the raw radiance map by adaptively combining the outputs of the two networks that have different characteristics with complementary information. Finally, a full-color HDR image is obtained by interpolating missing color information. Experimental results show that the proposed algorithm outperforms conventional algorithms significantly on various datasets. The source codes and pretrained models are available at https://github.com/viengiaan/EDWL."
Seeing through a Black Box: Toward High-Quality Terahertz Imaging via Subspace-and-Attention Guided Restoration
Authors: Weng-Tai Su; Yi-Chun Hung; Po-Jen Yu; Shang-Hua Yang; Chia-Wen Lin
Venue: ECCV 2022 (main)

Abstract: "Terahertz (THz) imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The performances of existing restoration methods are highly constrained by the diffraction-limited THz signals. To address the problem, we propose a novel Subspace-and-Attention-guided Restoration Network (SARNet) that fuses multi-spectral features of a THz image for effective restoration. To this end, SARNet uses multi-scale branches to extract spatio-spectral features of amplitude and phase which are then fused via shared subspace projection and attention guidance. Here, we experimentally construct a THz time-domain spectroscopy system covering a broad frequency range from 0.1 THz to 4 THz for building up temporal/spectral/spatial/phase/material THz database of hidden 3D objects. Complementary to a quantitative evaluation, we demonstrate the effectiveness of SARNet on 3D THz tomographic reconstruction applications."
Tomography of Turbulence Strength Based on Scintillation Imaging
Authors: Nir Shaul; Yoav Y. Schechner
Venue: ECCV 2022 (main)

Abstract: "Developed areas have plenty of artificial light sources. As the stars, they appear to twinkle, i.e., scintillate. This effect is caused by random turbulence. We leverage this phenomenon in order to reconstruct the spatial distribution the turbulence strength (TS). Sensing is passive, using a multi-view camera setup in a city scale. The cameras sense the scintillation of light sources in the scene. The scintillation signal has a linear model of a line integral over the field of TS. Thus, the TS is recovered by linear tomography analysis. Scintillation offers measurements and TS recovery, which are more informative than tomography based on angle-of-arrival (projection distortion) statistics. We present the background and theory of the method. Then, we describe a large field experiment to demonstrate this idea, using distributed imagers. As far as we know, this work is the first to propose reconstruction of a TS horizontal field, using passive optical scintillation measurements."
Realistic Blur Synthesis for Learning Image Deblurring
Authors: Jaesung Rim; Geonung Kim; Jungeon Kim; Junyong Lee; Seungyong Lee; Sunghyun Cho
Venue: ECCV 2022 (main)

Abstract: "Training learning-based deblurring methods demands a tremendous amount of blurred and sharp image pairs. Unfortunately, existing synthetic datasets are not realistic enough, and deblurring models trained on them cannot handle real blurred images effectively. While real datasets have recently been proposed, they provide limited diversity of scenes and camera settings, and capturing real datasets for diverse settings is still challenging. To resolve this, this paper analyzes various factors that introduce differences between real and synthetic blurred images. To this end, we present RSBlur, a novel dataset with real blurred images and the corresponding sharp image sequences to enable a detailed analysis of the difference between real and synthetic blur. With the dataset, we reveal the effects of different factors in the blur generation process. Based on the analysis, we also present a novel blur synthesis pipeline to synthesize more realistic blur. We show that our synthesis pipeline can improve the deblurring performance on real blurred images."
Learning Phase Mask for Privacy-Preserving Passive Depth Estimation
Authors: Zaid Tasneem; Giovanni Milione; Yi-Hsuan Tsai; Xiang Yu; Ashok Veeraraghavan; Manmohan Chandraker; Francesco Pittaluga
Venue: ECCV 2022 (main)

Abstract: "With over a billion sold each year, cameras are not only becoming ubiquitous, but are driving progress in a wide range of domains such as mixed reality, robotics, and more. However, severe concerns regarding the privacy implications of camera-based solutions currently limit the range of environments where cameras can be deployed. The key question we address is: Can cameras be enhanced with a scalable solution to preserve users’ privacy without degrading their machine intelligence capabilities? Our solution is a novel end-to-end adversarial learning pipeline in which a phase mask placed at the aperture plane of a camera is jointly optimized with respect to privacy and utility objectives. We conduct an extensive design space analysis to determine operating points with desirable privacy-utility tradeoffs that are also amenable to sensor fabrication and real-world constraints. We demonstrate the first working prototype that enables passive depth estimation while inhibiting face identification."
LWGNet – Learned Wirtinger Gradients for Fourier Ptychographic Phase Retrieval
Authors: Atreyee Saha; Salman S. Khan; Sagar Sehrawat; Sanjana S. Prabhu; Shanti Bhattacharya; Kaushik Mitra
Venue: ECCV 2022 (main)

Abstract: "Fourier Ptychographic Microscopy (FPM) is an imaging procedure that overcomes the traditional limit on Space-Bandwidth Product (SBP) of conventional microscopes through computational means. It utilizes multiple images captured using a low numerical aperture (NA) objective and enables high-resolution phase imaging through frequency domain stitching. Existing FPM reconstruction methods can be broadly categorized into two approaches: iterative optimization based methods, which are based on the physics of the forward imaging model, and data-driven methods which commonly employ a feed-forward deep learning framework. We propose a hybrid model-driven residual network that combines the knowledge of the forward imaging system with a deep data-driven network. Our proposed architecture, LWGNet, unrolls traditional Wirtinger flow optimization algorithm into a novel neural network design that enhances the gradient images through complex convolutional blocks. Unlike other conventional unrolling techniques, LWGNet uses fewer stages while performing at par or even better than existing traditional and deep learning techniques, particularly, for low-cost and low dynamic range CMOS sensors. This improvement in performance for low-bit depth and low-cost sensors has the potential to bring down the cost of FPM imaging setup significantly. Finally, we show consistently improved performance on our collected real data."
PANDORA: Polarization-Aided Neural Decomposition of Radiance
Authors: Akshat Dave; Yongyi Zhao; Ashok Veeraraghavan
Venue: ECCV 2022 (main)

Abstract: "Reconstructing an object’s geometry and appearance from multiple images, also known as inverse rendering, is a fundamental problem in computer graphics and vision. Inverse rendering is inherently ill-posed because the captured image is an intricate function of unknown lighting, material properties and scene geometry. Recent progress in representing scene through coordinate-based neural networks has facilitated inverse rendering resulting in impressive geometry reconstruction and novel-view synthesis. Our key insight is that polarization is a useful cue for neural inverse rendering as polarization strongly depends on surface normals and is distinct for diffuse and specular reflectance. With the advent of commodity on-chip polarization sensors, capturing polarization has become practical. We propose PANDORA, a polarimetric inverse rendering approach based on implicit neural representations. From multi-view polarization images of an object, PANDORA jointly extracts the object’s 3D geometry, separates the outgoing radiance into diffuse and specular and estimates the incident illumination. We show that PANDORA outperforms state-of-the-art radiance decomposition techniques. PANDORA outputs clean surface reconstructions free from texture artefacts, models strong specularities accurately and estimates illumination under practical unstructured scenarios."
HuMMan: Multi-modal 4D Human Dataset for Versatile Sensing and Modeling
Authors: Zhongang Cai; Daxuan Ren; Ailing Zeng; Zhengyu Lin; Tao Yu; Wenjia Wang; Xiangyu Fan; Yang Gao; Yifan Yu; Liang Pan; Fangzhou Hong; Mingyuan Zhang; Chen Change Loy; Lei Yang; Ziwei Liu
Venue: ECCV 2022 (main)

Abstract: "4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications. With the advances of new sensors and algorithms, there is an increasing demand for more versatile datasets. In this work, we contribute HuMMan, a large-scale multi-modal 4D human dataset with 1000 human subjects, 400k sequences and 60M frames. HuMMan has several appealing properties: 1) multi-modal data and annotations including color images, point clouds, keypoints, SMPL parameters, and textured meshes; 2) popular mobile device is included in the sensor suite; 3) a set of 500 actions, designed to cover fundamental movements; 4) multiple tasks such as action recognition, pose estimation, parametric human recovery, and textured mesh reconstruction are supported and evaluated. Extensive experiments on HuMMan voice the need for further study on challenges such as fine-grained action recognition, dynamic human mesh reconstruction, point cloud-based parametric human recovery, and cross-device domain gaps."
DVS-Voltmeter: Stochastic Process-Based Event Simulator for Dynamic Vision Sensors
Authors: Songnan Lin; Ye Ma; Zhenhua Guo; Bihan Wen
Venue: ECCV 2022 (main)

Abstract: "Recent advances in deep learning for event-driven applications with dynamic vision sensors (DVS) primarily rely on training over simulated data. However, most simulators ignore various physics-based characteristics of real DVS, such as the fidelity of event timestamps and comprehensive noise effects. We propose an event simulator, dubbed DVS-Voltmeter, to enable high-performance deep networks for DVS applications. DVS-Voltmeter incorporates the fundamental principle of physics - (1) voltage variations in a DVS circuit, (2) randomness caused by photon reception, and (3) noise effects caused by temperature and parasitic photocurrent - into a stochastic process. With the novel insight into the sensor design and physics, DVS-Voltmeter generates more realistic events, given high frame-rate videos. Qualitative and quantitative experiments show that the simulated events resemble real data. The evaluation on two tasks, i.e., semantic segmentation and intensity-image reconstruction, indicates that neural networks trained with DVS-Voltmeter generalize favorably on real events against state-of- the-art simulators."
Benchmarking Omni-Vision Representation through the Lens of Visual Realms
Authors: Yuanhan Zhang; Zhenfei Yin; Jing Shao; Ziwei Liu
Venue: ECCV 2022 (main)

Abstract: "Though impressive performance has been achieved in specific visual realms (\eg faces, dogs, and places), an omni-vision representation that can generalize to many natural visual domains is highly desirable. Nonetheless, the existing benchmark for evaluating visual representations, such as ImageNet, VTAB-natural, and CLIP benchmark suite, is either limited in the spectrum of realms or built by arbitrarily integrating the current datasets. In this paper, we propose Omni-Realm Benchmark (OmniBenchmark) that enables systematically measuring the generalization ability across a wide range of visual realms. OmniBenchmark firstly integrates the concepts from Wikidata to enlarge the storage of concepts of each sub-tree of WordNet. Then, it leverages expert knowledge from WordNet to define a comprehensive spectrum of 21 semantic realms in the natural domain, which is twice of ImageNet’s. Finally, we manually annotate all 7,372 valid concepts, forming a 21-realm dataset with 1,074,346 images. With OmniBenchmark, we propose a hierarchical instance contrastive learning framework for learning better omni-vision representation, \ie Relational Contrastive learning (ReCo), boosting the performance of representation learning across omni-realms. As the hierarchical semantic relation naturally emerges in the label system of visual datasets, ReCo attracts the representations within the same semantic realm during pre-training, facilitating the model converges faster than conventional contrastive learning when ReCo is further fine-tuned to the specific realm. Extensive experiments demonstrate the superior performance of ReCo over state-of-the-art contrastive learning methods on both ImageNet and OmniBenchmark. Beyond that, We conduct a systematic investigation of recent advances in both architectures (from CNNs to transformers) and learning paradigms (from supervised learning to self-supervised learning) on our benchmark. Multiple practical observations are revealed to facilitate future research."
BEAT: A Large-Scale Semantic and Emotional Multi-modal Dataset for Conversational Gestures Synthesis
Authors: Haiyang Liu; Zihao Zhu; Naoya Iwamoto; Yichen Peng; Zhengqing Li; You Zhou; Elif Bozkurt; Bo Zheng
Venue: ECCV 2022 (main)

Abstract: "Achieving realistic, vivid, and human-like synthesized conversational gestures conditioned on multi-modal data is still an unsolved problem due to the lack of available datasets, models and standard evaluation metrics. To address this, we build Body-Expression-Audio-Text dataset, BEAT, which has i) 76 hours, high-quality, multi-modal data captured from 30 speakers talking with eight different emotions and in four different languages, ii) 32 millions frame-level emotion and semantic relevance annotations. Our statistical analysis on BEAT demonstrates the correlation of conversational gestures with facial expressions, emotions, and semantics, in addition to the known correlation with audio, text, and speaker identity. Based on this observation, we propose a baseline model, Cascaded Motion Network (CaMN), which consists of above six modalities modeled in a cascaded architecture for gesture synthesis. To evaluate the semantic relevancy, we introduce a metric, Semantic Relevance Gesture Recall (SRGR). Qualitative and quantitative experiments demonstrate metrics’ validness, ground truth data quality, and baseline’s state-of-the-art performance. To the best of our knowledge, BEAT is the largest motion capture dataset for investigating human gestures, which may contribute to a number of different research fields, including controllable gesture synthesis, cross-modality analysis, and emotional gesture recognition. The data, code and model are available on https://pantomatrix.github.io/BEAT/"