Real Photographs Research Articles

Cartoon-Style Image Rendering Transfer Based on Neural Networks.

Cartoon rendering of images is a challenging nonphotorealistic image rendering task, which aims to transform real photos into cartoon-style nonphotorealistic images while preserving the semantic content and texture details of the original photos. Based on the understanding of the characteristics of cartoon images and analysis of the defects of established approaches, this paper improves existing methods. The convolutional neural networks' powerful image processing ability and attention mechanism are utilized to get more fine-grained image features, making the result of rendering more realistic. This paper mainly studies how neural networks can better process cartoon pictures, adjust parameters, and choose training methods. Furthermore, the article proposes a new solution of cartoon-style image rendering based on deep learning. The actual test results of real original images have shown that the model is suitable for cartoon-style image rendering.

High dynamic range and super-resolution from raw image bursts

Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in saturated areas. This paper introduces the first approach (to the best of our knowledge) to the reconstruction of highresolution, high-dynamic range color images from raw photographic bursts captured by a handheld camera with exposure bracketing. This method uses a physically-accurate model of image formation to combine an iterative optimization algorithm for solving the corresponding inverse problem with a learned image representation for robust alignment and a learned natural image prior. The proposed algorithm is fast, with low memory requirements compared to state-of-the-art learning-based approaches to image restoration, and features that are learned end to end from synthetic yet realistic data. Extensive experiments demonstrate its excellent performance with super-resolution factors of up to ×4 on real photographs taken in the wild with hand-held cameras, and high robustness to low-light conditions, noise, camera shake, and moderate object motion.

PID Controller-Guided Attention Neural Network Learning for Fast and Effective Real Photographs Denoising.

Real photograph denoising is extremely challenging in low-level computer vision since the noise is sophisticated and cannot be fully modeled by explicit distributions. Although deep-learning techniques have been actively explored for this issue and achieved convincing results, most of the networks may cause vanishing or exploding gradients, and usually entail more time and memory to obtain a remarkable performance. This article overcomes these challenges and presents a novel network, namely, PID controller guide attention neural network (PAN-Net), taking advantage of both the proportional-integral-derivative (PID) controller and attention neural network for real photograph denoising. First, a PID-attention network (PID-AN) is built to learn and exploit discriminative image features. Meanwhile, we devise a dynamic learning scheme by linking the neural network and control action, which significantly improves the robustness and adaptability of PID-AN. Second, we explore both the residual structure and share-source skip connections to stack the PID-ANs. Such a framework provides a flexible way to feature residual learning, enabling us to facilitate the network training and boost the denoising performance. Extensive experiments show that our PAN-Net achieves superior denoising results against the state-of-the-art in terms of image quality and efficiency.

Realistic simulation of hydraulic erosion on slope

AbstractHydraulic erosion plays an important role in the formation of landform. Influenced by runoff, soil is destroyed, denuded, and transported to form a specific landform. The simulation of erosion can not only be used for the construction of realistic terrain, but also for evaluation in soil and water conservation, which has important research and application value. We construct a hydraulic erosion simulation framework on the basis of soil and water conservation's theory, which considers various factors in erosion, including: water flow, infiltration, transportation, erosion, ditch widening, deposition, and soil sliding. It is the first time to consider the generation time of runoff, soil fixation of plant roots, and ditch widening phenomenon in simulation. Using this method, we can simulate ditch evolution process from rill, shallow ditch, cut ditch to gully, which consist with the real process. Compared with real photos and data, our method has authenticity in both visual and theoretical aspects.

OP0004 AUTOMATED RECOGNITION AND MONITORING OF DORSAL SKIN FOLDS BY A CONVOLUTIONAL NEURAL NETWORK AS A POTENTIAL DIGITAL BIOMARKER FOR JOINT SWELLING IN PATIENTS WITH RHEUMATOID ARTHRITIS

BackgroundTo monitor rheumatic diseases, digital biomarkers such as wearables are of increasing interest, but they lack disease specificity.ObjectivesIn this study, we apply convolution neural networks (CNN) to real world hand photographs in order to automatically detect, extract and analyse dorsal finger folds lines as a correlate of proximal interphalangeal (PIP) joint swelling in patients with rheumatoid arthritis (RA).MethodsHand photographs from 190 RA patients were taken by a smartphone camera in a standardized manner. PIP joints were categorised as either swollen or not swollen based on clinical judgement and ultrasound. Images were automatically preprocessed by cropping PIP joints and extracting dorsal finger folds. Subsequently, metrical analysis of dorsal finger folds was performed and a CNN was trained to classify the dorsal finger lines into swollen versus non-swollen joints. Representative horizontal finger folds were also quantified in a subset of patients before and after resolution of PIP swelling and in patients with disease flares, respectively.ResultsIn swollen joints, the number of automatically extracted double-contoured, deep skinfold imprints was significantly reduced compared to non-swollen joints (1.3, SD 0.8 vs. 3.3, SD 0.49). The joint diameter / deep skinfold ratio was significantly higher in swollen (4.1, SD 1.4) versus non-swollen joints (2.1 SD 0.6). The CNN model successfully differentiated swollen from non-swollen joints based on finger fold patterns with a validation accuracy of 0.84. A heatmap of the original images obtained by an extraction algorithm confirmed finger folds as the region of interest for correct classification. After significant response to DMARD +/- corticosteroid therapy, longitudinal metrical analysis of eight representative deep finger folds showed a decrease of the mean diameter/ finger fold length (finger fold index, FFl) from 3.03 (SD 0.68) to 2.08 (SD 0.57). Conversely, the FFI increased in patients with a flare of joint swelling.ConclusionAutomated preprocessing and the application of CNN algorithms in combination with longitudinal metrical analysis of dorsal finger fold patterns extracted from real world hand photos might serve as a digital biomarker in RA.Figure 1.Automated finger fold recognition to monitor rheumatoid arthritis (RA). Hand photographs are taken by a smartphone (A). Hands, and subsequently proximal interphalangeal (PIP) joints, are automatically recognized and extracted. Finger fold lines are isolated from the images, measured and related to the joint diameter (B,C). A convolutional deep neural network was used to train a model for classification of extracted finger fold patterns into swollen vs. non swollen joints (D). On cropped PIP joint images, the heatmap of the same classification task confirms finger folds as the region of interest (E).Disclosure of InterestsThomas Hügle Shareholder of: Atreon SA., Speakers bureau: Multiple. Not relevant for this work., Grant/research support from: Multiple. Not for this work., Leo Caratsch: None declared, Matteo Matteo Caorsi Employee of: MC is an employee of L2F., Jules Maglione: None declared, Diana Dan: None declared, Alexandre Dumusc Speakers bureau: Multiple. Not relevant for this work., Marc Blanchard Shareholder of: Atreon SA., Gabriel Kalweit: None declared, Maria Kalweit: None declared.

Species re-identification based on photographs of Ocean sunfishes (Family Molidae) from Philippine waters, including reconfirmation of Mola alexandrini

Detailed biological information (e.g. distribution, size, sex) on ocean sunfishes/molids (Molidae) in Philippine waters is lacking. Four species of ocean sunfishes from Philippine waters are known to date: Mola mola (Linnaeus, 1758), Mola alexandrini (Ranzani, 1839), Masturus lanceolatus (Liénard, 1840), and Ranzania laevis (Pennant, 1776). However, in recent years, the taxonomy of Molidae (especially the genus Mola) has progressed at the global level, and it becomes necessary to review species that occur in Philippine waters. In this study, photographs of Molidae from Philippine waters were collected and morphological research involving museum specimens was conducted. The results of species re-identification based on the photographs of 12 specimens of Molidae obtained in this study were as follows: a specimen of M. alexandrini, five specimens of M. mola, and six specimens of Ma. lanceolatus. Although a specimen identified as M. alexandrini in a previous study was here re-identified as M. mola, the occurrence of M. alexandrini in Philippine waters was nevertheless confirmed in this study on the basis of another specimen. Mola alexandrini and Ma. lanceolatus were confused with M. mola locally, suggesting the importance of preserving real specimens, photographs and genetic samples.

DOG-ADTCP: A new feature descriptor for protection of face identification system

Nowadays the face biometric system is gaining popularity for authentication in access control applications. At the same time, the threats to authentication systems are also increased in terms of spoofing or presentation attacks where an intruder attempts to spoof the face recognition system by using genuine user photos or video to gain access. In order to effectively secure the secrecy of a genuine user, there is an urgent need for building a face authentication system with anti-spoofing countermeasures. In this paper, we introduce a novel face anti-spoofing approach, which is mainly based on contrast and texture features of both real and spoof photos. A modified Difference of Gaussian (DOG) filtering method and Angle Directional Ternary Co-relation pattern (ADTCP) are used. The publicly available NUAA, MSU-MFSD and REPLAY-ATTACK photo-impostor datasets are tested on the approach, which includes images with different illumination and area of the face. The accuracy of the proposed approach is evaluated using different metrics. The results show that our proposed method is superior to other state-of-the-art (SOTA) practices when tested on three photo-impostor datasets.

Automatic Object Detection Algorithm-Based Braille Image Generation System for the Recognition of Real-Life Obstacles for Visually Impaired People.

The global prevalence of visual impairment due to diseases and accidents continues to increase. Visually impaired individuals rely on their auditory and tactile senses to recognize surrounding objects. However, accessible public facilities such as tactile pavements and tactile signs are installed only in limited areas globally, and visually impaired individuals use assistive devices such as canes or guide dogs, which have limitations. In particular, the visually impaired are not equipped to face unexpected situations by themselves while walking. Therefore, these situations are becoming a great threat to the safety of the visually impaired. To solve this problem, this study proposes a living assistance system, which integrates object recognition, object extraction, outline generation, and braille conversion algorithms, that is applicable both indoors and outdoors. The smart glasses guide objects in real photos, and the user can detect the shape of the object through a braille pad. Moreover, we built a database containing 100 objects on the basis of a survey to select objects frequently used by visually impaired people in real life to construct the system. A performance evaluation, consisting of accuracy and usefulness evaluations, was conducted to assess the system. The former involved comparing the tactile image generated on the basis of braille data with the expected tactile image, while the latter confirmed the object extraction accuracy and conversion rate on the basis of the images of real-life situations. As a result, the living assistance system proposed in this study was found to be efficient and useful with an average accuracy of 85% a detection accuracy of 90% and higher, and an average braille conversion time of 6.6 s. Ten visually impaired individuals used the assistance system and were satisfied with its performance. Participants preferred tactile graphics that contained only the outline of the objects, over tactile graphics containing the full texture details.

Face photo-sketch synthesis based on multi-scale feature fusion

The synthesis of face sketches from real face photos and its inverse process have a wide range of applications, such as digital entertainment and assisting in the investigation of criminal cases. However, due to the significant discrepancies in texture between photos and sketches, the transformation between them is still a challenging problem. Recently, the methods based on generative adversarial nets (GANs) have shown encouraging results in the transformation between images, especially the transformation from photos to sketches. However, most of them produce different deformations or blur in the key components of the face, which affects the realism of the synthetic image. To address this challenge, we propose a novel face photo-sketch synthesis algorithm based on multi-scale feature fusion to improve the structural integrity and texture fidelity of the synthetic image. Firstly, an encoder is utilized to extract the multi-scale coding features of the input image. Then the bottom coding features are passed by a dilated convolution module and transmitted to the decoder for decoding. In the decoding process, the decoding features of different scales and the coding features of corresponding scales are concatenated in the channel dimension to obtain the multi-scale coding and decoding fusion features. Finally, the coding and decoding fusion features of different scales are further fused at the output of the decoder and the final synthesis result is generated through a convolution layer. By combining the features of different scales in the encoding and decoding processes in the channel dimension at the same time, we can maintain good image structure and texture details and generate realistic face sketch/photo images. We verify the effectiveness of the proposed method in several challenging datasets. The quantitative and qualitative evaluation shows that the proposed method is superior to other state-of-the-art methods in generating face sketches (or photos) with high visual quality.

Spectroscopic analysis of vintage hand-colored real photo postcard of French stage actress Melle Charclais by Reutlinger, Paris

This article reviews the history of real photo postcards and hand-coloring practices to draw attention to their historical and artistic value, and the significance of their preservation. A real photo postcard of French stage actress, Melle Charclais, dating back to the early 1900s’ and taken by Léopold Reutlinger, a famous German photographer, was selected to acquire knowledge on the materials and techniques used at the time to develop an understanding of their deterioration paths; and accordingly, make correct preservation choices. This particular real photo postcard was chosen for its esthetic value since it represents an unusual art nouveau style of postcard designs integrating photography with hand-coloring and bead art. The photographic process used was identified as silver gelatin process using a USB digital microscope. Analyses performed using scanning electron microscopy with energy-dispersive X-ray spectroscopy, attenuated total reflectance—Fourier transform infrared spectroscopy and Raman spectroscopy revealed the presence of Hematite, Cobalt Blue, and Naples Yellow.

Creation and validation of a photonumeric scale for assessment of lip fullness.

BackgroundValidated, objective clinical scales are needed to assess aesthetic improvement of the lips after augmentation with dermal fillers.ObjectiveTo develop a lip fullness rating scale and establish its reliability for grading subjects in clinical trials or routine practice, and sensitivity for detecting clinically meaningful changes.MethodsThe Teoxane Lip Fullness Scale (TLFS), a proprietary, 5‐grade photonumeric scale, was developed by clinical experts based on real subject photographs and was validated through both photographic and live subjects' evaluation.ResultsClinician intra‐ and inter‐rater agreement for the TLFS was substantial to almost perfect. Mean intra‐rater weighted Kappa score between the two rounds of photographic validation was 0.92, and inter‐rater agreement was substantial with an ICC of 0.93 for the combined rounds. Average intra‐rater weighted Kappa score and inter‐rater ICC for the live validation were equally high, reaching 0.91 and 0.89 respectively. Additionally, evaluators identified clinically significant differences between photographs of subjects presenting a 1‐grade or 2‐grade difference on the scale in 90% and 98% of cases, respectively.ConclusionsThe intra‐rater Kappa scores and inter‐rater ICC met their pre‐determined acceptance criteria of >0.70 in the photographic and live validation. The TLFS was shown to be a repeatable and reproducible Clinician Reported Outcome (Clin‐RO) for healthcare providers to classify lip fullness both in clinical trials and in routine patient care. A 1‐grade difference on the TLFS can detect a clinically meaningful difference in lip fullness.

Usefulness of telemedicine in immediate postoperative follow-up after ICD and pacemaker implantation. A new tool for outpatient surgery

Several complications can occur in immediate post operative period following ICD and pacemaker implantation. Telemedicine appears to be very interesting in that period to reduce this particular risk. The aim of this study is to test a new tool called “Suivi Médical Postopératoire Sécurisé” developed by Avismedic.fr®, in the particular field of PM and ICD implantation. This tool creates an electronic and secure medical file specific to each patient. The rythmologist, the regular doctor, the nurses in charge of postoperative care have a specific and secure access via internet to the file, creating a specific network. They can communicate and exchange in real time, share documents, photographs, prescriptions and biological data. The rhythmologist can deliver medical advice. Anonymous alerts are delivered by SMS and email, inviting them to connect if a new information is available, or if a contact is needed. This specific electronic file is archived for a ten years period, in case of medico-legal problem. This tool is already used in orthopeadic surgery and in outpatient surgery. One hundred and two consecutive patients who had a PM or ICD implantation (71 PM and 31 ICD) in our hospital between November 2019 and April 2021 were proposed included in the study. All accepted. Ninety-eight nurses (96%) accepted to carry out the follow-up with this electronic tool. Eighty-seven (85%) regular doctors accepted to be included in the network. Eighty-seven (85%) nurses did correctly the follow-up with regular exchange and systemic electronic report and photograph of the scar at each visit. In 5 cases (5.7%), complications like haematoma, local oedema… could be rapidly managed, avoiding more consequent complications or hospitalizations. For 20 patients, supplementary prescriptions were electronically delivered to the patients. This new tool appears well accepted, very effective and very interesting, especially for outpatient surgery development.

Simulation evaluation of knitted mesh structure using morphological operations

High-quality simulation of fabric structural features is significant for realizing the morphological prediction of nonuniform mesh structures and building numerical simulation of physical properties. For lace textiles which have hundreds and thousands of variable meshes, an objective and unified standard to evaluate structural deformation is quite challenging. It needs to comprehensively and quantitatively conclude a result, instead of visual and subjective judgment. Therefore, this paper proposed an image-based method to mathematically solve accuracy by comparing morphological features from both simulation result and real fabric photograph. Jacquardtronic lace textiles were fabricated as experimental samples. Based on morphological expansion and corrosion algorithms, non-characteristic lapping details were eroded from binary images with only featured single-pixel contours of irregular meshes. Shape descriptor of each featured image was represented by a moment vector of seven Hu invariant moments. Then the morphological vectors of both simulated and real fabric images were substituted into a defined equation of similarity measurement. This image-based evaluation model effectively avoids defects of subjective visual observation and geometric measurement methods.

Research of Rotor Unmanned Aerial Vehicle (UAV) Production and GIS Integration Based on 3D Panorama

3D panorama technology is a kind of virtual reality technology based on image creation, which is used real photos to show the scene to achieve the effect of immersive virtual roaming, and improve the interactivity between the network platform with the user. Panoramic images, UAV remote sensing and GIS technology is the application and development of new technology of surveying and mapping industry, but apply combination is rarely studied. In this paper, based on rotor unmanned aerial vehicle (UAV) fixed-point hover took photos of different angles using PTGui software to create panorama images stitching, optimization, software reuse Pano2VR making 3D roaming figure, and compared with Supermap GIS integration. Based on a downtown building community will be released on GIS 3D roaming figure, you can see, after the integration of 3 d panorama with two-dimensional GIS geospatial information, also can see the area the real scene. The surveying and mapping, tourism and other industries to provide a new technical method.

CariMe: Unpaired Caricature Generation With Multiple Exaggerations

Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from generic image-to-image translation, drawing caricatures automatically is a more challenging task due to the existence of various spatial deformations. Previous caricature generation methods are obsessed with predicting definite image warping from a given photo while ignoring the intrinsic representation and distribution of geometric exaggerations in caricatures. This limits their ability on diverse exaggeration generation. In this paper, we generalize the caricature generation problem from instance-level warping prediction to distribution-level deformation modeling. Based on this assumption, we present the first exploration for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unpaired CARIcature generation with Multiple Exaggerations (CariMe)</i> . Technically, we propose a Multi-exaggeration Warper network to learn the distribution-level mapping from photos to facial exaggerations. This makes it possible to generate diverse and reasonable exaggerations from randomly sampled warp codes given one input photo. To better represent the facial exaggeration and produce fine-grained warping, a deformation-field-based warping method is also proposed, which captures more detailed exaggerations than previous point-based warping methods. Experiments and two perceptual studies prove the superiority of our method comparing with other state-of-the-art methods, showing the improvement of our work on caricature generation. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/edward3862/CariMe-pytorch</uri> .

An Analysis of the Activities of the Radical Ideological Movement “Columbine” Followers

The paper examines the cases of attacks on schools in Russia, assesses the events that have occurred, and analyzes the activities of school shooters. The symbolism and attributes of Columbine’s followers are given. As part of the study, a content analysis of the Internet information space was carried out, which allowed us to draw certain conclusions on the spread of the radical ideological movement Columbine. Thus, a typical representative of a “Columbine community” is from 14 to 20 years old. Teenagers often pretend to be older in the virtual space and maintain several accounts in the same social network. The formation of a teenager’s personality occurs through the prism of relationships in the reference group. If this self-identification does not occur in the family or school for various reasons, then the minor plunges into the information and network space, where he finds comfort, his interests and views are shared, and he gets support. A teenager gets an impression that he is best understood in death groups, Columbine communities and other destructive associations, and not in the family. The information that these users leave on their personal social network accounts allows us to identify a number of features indicating depression, the presence of pre-suicidal syndrome, aggressive behavior and excessive cruelty. Thus, the following conclusion can be drawn: a typical participant in a Columbine community is an emotionally unstable, driven, complex, secretive, insecure individual. As a rule, there are few or no real photos on the account of a typical Columbine community participant. Sometimes a pseudonym is used instead of the real name: in most cases it is associated with the Columbine cult. In addition, the account of such a teenager contains photo and video content that can be attributed to suicidal themes, excessive cruelty and aggression, and a passion for weapons.

Learning to reconstruct botanical trees from single images

We introduce a novel method for reconstructing the 3D geometry of botanical trees from single photographs. Faithfully reconstructing a tree from single-view sensor data is a challenging and open problem because many possible 3D trees exist that fit the tree's shape observed from a single view. We address this challenge by defining a reconstruction pipeline based on three neural networks. The networks simultaneously mask out trees in input photographs, identify a tree's species, and obtain its 3D radial bounding volume - our novel 3D representation for botanical trees. Radial bounding volumes (RBV) are used to orchestrate a procedural model primed on learned parameters to grow a tree that matches the main branching structure and the overall shape of the captured tree. While the RBV allows us to faithfully reconstruct the main branching structure, we use the procedural model's morphological constraints to generate realistic branching for the tree crown. This constraints the number of solutions of tree models for a given photograph of a tree. We show that our method reconstructs various tree species even when the trees are captured in front of complex backgrounds. Moreover, although our neural networks have been trained on synthetic data with data augmentation, we show that our pipeline performs well for real tree photographs. We evaluate the reconstructed geometries with several metrics, including leaf area index and maximum radial tree distances.

Fruit quality and defect image classification with conditional GAN data augmentation

Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy fruit from undesirable fruit, such as those which are mouldy or damaged. State-of-the-art works in the field report high accuracy results on small datasets (<1000 images), which are not representative of the population regarding real-world usage. The goals of this study are to further enable real-world usage by improving generalisation with data augmentation as well as to reduce overfitting and energy usage through model pruning. In this work, we suggest a machine learning pipeline that combines the ideas of fine-tuning, transfer learning, and generative model-based training data augmentation towards improving fruit quality image classification. A linear network topology search is performed to tune a VGG16 lemon quality classification model using a publicly-available dataset of 2690 images. We find that appending a 4096 neuron fully connected layer to the convolutional layers leads to an image classification accuracy of 83.77%. We then train a Conditional Generative Adversarial Network on the training data for 2000 epochs, and it learns to generate relatively realistic images. Grad-CAM analysis of the model trained on real photographs shows that the synthetic images can exhibit classifiable characteristics such as shape, mould, and gangrene. A higher image classification accuracy of 88.75% is then attained by augmenting the training with synthetic images, arguing that Conditional Generative Adversarial Networks have the ability to produce new data to alleviate issues of data scarcity. Finally, model pruning is performed via polynomial decay, where we find that the Conditional GAN-augmented classification network can retain 81.16% classification accuracy when compressed to 50% of its original size.

Machine Learning Generated Synthetic Faces for Use in Facial Aesthetic Research.

Importance: A centralized repository of clinically applicable facial images with unrestricted use would facilitate facial aesthetic research. Objective: Using a machine learning neural network, we aim to (1) create a repository of synthetic faces that can be used for facial aesthetic research and (2) analyze synthetic faces according to contemporary aesthetic principles. Design, Setting, and Participants: Synthetic facial images were generated using an open source generative adversarial network. Images were refined and then analyzed using computer vision technology. Interventions: Not applicable. Main Outcomes and Measures: Synthetic facial images were created for use as a facial aesthetic research data set. Results: One thousand synthetic images were generated, and 60 images underwent analysis. Image attributes, including age, gender, image principle axis, facial emotion, and facial landmark points, were attained. Images demonstrated accordance with contemporary aesthetic principles of horizontal thirds and vertical fifths. Images demonstrated excellent correspondence when compared with real human facial photographs. Conclusions and Relevance: We have generated realistic synthetic facial images that have potential as a valuable research tool and demonstrate similarity to real human photographs while adhering to contemporary aesthetic principles.

Relighting Humans in the Wild: Monocular Full‐Body Human Relighting with Domain Adaptation

AbstractThe modern supervised approaches for human image relighting rely on training data generated from 3D human models. However, such datasets are often small (e.g., Light Stage data with a small number of individuals) or limited to diffuse materials (e.g., commercial 3D scanned human models). Thus, the human relighting techniques suffer from the poor generalization capability and synthetic‐to‐real domain gap. In this paper, we propose a two‐stage method for single‐image human relighting with domain adaptation. In the first stage, we train a neural network for diffuse‐only relighting. In the second stage, we train another network for enhancing non‐diffuse reflection by learning residuals between real photos and images reconstructed by the diffuse‐only network. Thanks to the second stage, we can achieve higher generalization capability against various cloth textures, while reducing the domain gap. Furthermore, to handle input videos, we integrate illumination‐aware deep video prior to greatly reduce flickering artifacts even with challenging settings under dynamic illuminations.