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The research aims to investigate how the 2D slot game “Lucky Hero”, created for this research, could convey the story of Beowulf. The result of this study could provide experience and strategy for improving narrative design in casual games with similar mechanics. Also, this study is valuable to developers/designers who want to explore the potential of using casual games, especially slot games, as a storytelling tool. To answer the research question “How could the 2D slot game Lucky Hero convey the story of Beowulf”, a “research for design” qualitative study was conducted. The researchers examined theories related to narrative design, design in slot machine games, and folk mythology to develop the game. Playtests followed by interviews and adapted stimulation recall were used as data collection methods, and thematic analysis was used as the data analysis technique. The findings revealed that the existing design of "Lucky Hero" was not capable of effectively conveying the story of Beowulf to players. However, the study identified key elements that influenced players' understanding, such as graphics and game mechanics. The future narrative design of “Lucky Hero” should focus on improving immersion, character representation, and the connection between game mechanics and the story.

Generative Neural Radiance Field (GNeRF) models, which extract implicit 3D representations from 2D images, have recently been shown to produce realistic images representing rigid/semi-rigid objects, such as human faces or cars. However, they usually struggle to generate high-quality images representing non-rigid objects, such as the human body, which is of a great interest for many computer graphics applications. This paper proposes a 3D-aware Semantic-Guided Generative Model (3D-SGAN) for human image synthesis, which combines a GNeRF with a texture generator. The former learns an implicit 3D representation of the human body and outputs a set of 2D semantic segmentation masks. The latter transforms these semantic masks into a real image, adding a realistic texture to the human appearance. Without requiring additional 3D information, our model can learn 3D human representations with a photo-realistic, controllable generation. Our experiments on the DeepFashion dataset show that 3D-SGAN significantly outperforms the most recent baselines. The code is available at https://github.com/zhangqianhui/3DSGAN, ECCV 2022. 29 pages

Due to the extreme environmental temperature variations, solutions that enable ultra-low thermal sensitivity in a mirror assembly are crucial for high-performance aerial optical imaging sensors (AOIS). Strategies such as the elimination of the coefficient of thermal expansion (CTE) mismatch and the employment of a flexure connection at the interface cannot be simply duplicated for the application involved, demanding specific design constraints. The contributions of support point number to the surface thermal sensitivity reduction and support stiffness improvement have been studied. A synthetic six-point support system that integrates equally spaced multiple ultra-low radial stiffness mirror flexure units and assembly external interface flexure units has been demonstrated on a 260 mm apertured annular mirror that involves significant CTE mismatch and demanding support stiffness constraint. The surface deformation RMS, due to the 35 °C temperature variation, is 16.7 nm.

3D-aware GANs based on generative neural radiance fields (GNeRF) have achieved impressive high-quality image generation, while preserving strong 3D consistency. The most notable achievements are made in the face generation domain. However, most of these models focus on improving view consistency but neglect a disentanglement aspect, thus these models cannot provide high-quality semantic/attribute control over generation. To this end, we introduce a conditional GNeRF model that uses specific attribute labels as input in order to improve the controllabilities and disentangling abilities of 3D-aware generative models. We utilize the pre-trained 3D-aware model as the basis and integrate a dual-branches attribute-editing module (DAEM), that utilize attribute labels to provide control over generation. Moreover, we propose a TRIOT (TRaining as Init, and Optimizing for Tuning) method to optimize the latent vector to improve the precision of the attribute-editing further. Extensive experiments on the widely used FFHQ show that our model yields high-quality editing with better view consistency while preserving the non-target regions. The code is available at https://github.com/zhangqianhui/TT-GNeRF., Comment: 14 pages

Controllable person image generation aims to produce realistic human images with desirable attributes (e.g., the given pose, cloth textures or hair style). However, the large spatial misalignment between the source and target images makes the standard architectures for image-to-image translation not suitable for this task. Most of the state-of-the-art architectures avoid the alignment step during the generation, which causes many artifacts, especially for person images with complex textures. To solve this problem, we introduce a novel Spatially-Adaptive Warped Normalization (SAWN), which integrates a learned flow-field to warp modulation parameters. This allows us to align person spatial-adaptive styles with pose features efficiently. Moreover, we propose a novel self-training part replacement strategy to refine the pretrained model for the texture-transfer task, significantly improving the quality of the generated cloth and the preservation ability of irrelevant regions. Our experimental results on the widely used DeepFashion dataset demonstrate a significant improvement of the proposed method over the state-of-the-art methods on both pose-transfer and texture-transfer tasks. The source code is available at https://github.com/zhangqianhui/Sawn., 12 pages

Achieving an efficient electrochemical nitrogen reduction reaction (ENRR) remains a great challenge, demanding the development of a new strategy for ENRR catalyst engineering. Herein, we demonstrate a largely improved ENRR by the controlled engineering of Ru nanowires with atomic Pt decoration. Specifically, the readily synthesized Ru88Pt12 nanowires exhibit a high NH3 production rate of 47.1 μg h-1 mgcat-1 and faradaic efficiency of 8.9% at -0.2 V, which are 5.3 and 14.6 times higher than those values for Ru nanowires. They also show outstanding stability, as evidenced by the full preservation of the NH3 yield and faradaic efficiency even after 15 h of electrocatalysis. As revealed by theoretical investigations, the d-band center of Ru atoms is upshifted by the tensile strain due to the presence of Pt atoms, leading to the selective enhancement of N2 adsorption and the stabilization of N2H∗. Such an atomic engineering method may be applied to precisely tailor other metal nanocatalysts for different applications. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) Accepted version This work was supported by the National Natural Science Foundation of China (21902119), Natural Science Foundation of Shandong Province (ZR2016BM12), AME-IRG grant (AMEIRG18- 0016) from Agency for Science, Technology and Research (A*STAR) of Singapore, and AcRF tier 2 grant (MOE2017-T2-2- 005) from Ministry of Education (Singapore).

Facial attribute editing aims to manipulate attributes on the human face, e.g., adding a mustache or changing the hair color. Existing approaches suffer from a serious compromise between correct attribute generation and preservation of the other information such as identity and background, because they edit the attributes in the imprecise area. To resolve this dilemma, we propose a progressive attention GAN (PA-GAN) for facial attribute editing. In our approach, the editing is progressively conducted from high to low feature level while being constrained inside a proper attribute area by an attention mask at each level. This manner prevents undesired modifications to the irrelevant regions from the beginning, and then the network can focus more on correctly generating the attributes within a proper boundary at each level. As a result, our approach achieves correct attribute editing with irrelevant details much better preserved compared with the state-of-the-arts. Codes are released at https://github.com/LynnHo/PA-GAN-Tensorflow., Comment: Code: https://github.com/LynnHo/PA-GAN-Tensorflow

Gaze correction aims to redirect the person's gaze into the camera by manipulating the eye region, and it can be considered as a specific image resynthesis problem. Gaze correction has a wide range of applications in real life, such as taking a picture with staring at the camera. In this paper, we propose a novel method that is based on the inpainting model to learn from the face image to fill in the missing eye regions with new contents representing corrected eye gaze. Moreover, our model does not require the training dataset labeled with the specific head pose and eye angle information, thus, the training data is easy to collect. To retain the identity information of the eye region in the original input, we propose a self-guided pretrained model to learn the angle-invariance feature. Experiments show our model achieves very compelling gaze-corrected results in the wild dataset which is collected from the website and will be introduced in details. Code is available at https://github.com/zhangqianhui/GazeCorrection.

PsbQ is an extrinsic subunit of the photosystem II in eukaryotic photosynthetic organisms. Numerous studies have demonstrated that PsbQ can stabilize the inorganic cofactors and enhance the oxygen release in PSII. The decrease of photosynthesis rate under salinity condition is normally attributed to the high concentration of injurious ions, such as Na(+) and Cl(-), which accumulate in the chloroplast and damage thylakoid membrane under salinity stress. In this study, AcPsbQ1 was isolated from a halophyte Atriplex canescens cDNA library. The AcPsbQ1 contains an open reading frame of 699 bp encoding a 233 amino acid protein. In order to investigate its function, AcPsbQ1 was cloned and transformed into Saccharomyces cerevisiae INVSc1. The heterologous expression of AcPsbQ1 in transgenic yeast significantly helped to increase the adapting and recovery ability of yeast cells under the salt and drought. Quantitative real-time PCR assay was performed to reveal the expression pattern of AcPsbQ1 under different abiotic stresses. On exposure to NaCl stress, the transcript level of AcPsbQ1 was significantly enhanced. AcPsbQ1 expression level was also up-regulated under drought stress. These results indicated that AcPsbQ1 might involve in the response to salt stress in A. canescens.

The paper studies the two main factors for the projection length distortion, presents the solving methods of eliminating projection length distortion value in high mountain areas when the projection length distortion value is bigger than 2.5cm/km. Including concentric reference ellipsoid method, map projection zone transformation method and compensating projection plane method. Engineering practice shows that the methods can effectively eliminate the errors brought by the projection length distortion.