3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses

3D-aware face generators are typically trained on 2D real-life face image datasets that primarily consist of near-frontal face data, and as such, they are unable to construct one-quarter headshot 3D portraits with complete head, neck, and shoulder geometry. Two reasons account for this issue: First, existing facial recognition methods struggle with extracting facial data captured from large camera angles or back views. Second, it is challenging to learn a distribution of 3D portraits covering the one-quarter headshot region from single-view data due to significant geometric deformation caused by diverse body poses. To this end, we first create the dataset 360{\deg}-Portrait-HQ (360{\deg}PHQ for short) which consists of high-quality single-view real portraits annotated with a variety of camera parameters (the yaw angles span the entire 360{\deg} range) and body poses. We then propose 3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator that learns a canonical 3D avatar distribution from the 360{\deg}PHQ dataset with body pose self-learning. Our model can generate view-consistent portrait images from all camera angles with a canonical one-quarter headshot 3D representation. Our experiments show that the proposed framework can accurately predict portrait body poses and generate view-consistent, realistic portrait images with complete geometry from all camera angles

Enhancing the Authenticity of Rendered Portraits with Identity-Consistent Transfer Learning

Despite rapid advances in computer graphics, creating high-quality photo-realistic virtual portraits is prohibitively expensive. Furthermore, the well-know ''uncanny valley'' effect in rendered portraits has a significant impact on the user experience, especially when the depiction closely resembles a human likeness, where any minor artifacts can evoke feelings of eeriness and repulsiveness. In this paper, we present a novel photo-realistic portrait generation framework that can effectively mitigate the ''uncanny valley'' effect and improve the overall authenticity of rendered portraits. Our key idea is to employ transfer learning to learn an identity-consistent mapping from the latent space of rendered portraits to that of real portraits. During the inference stage, the input portrait of an avatar can be directly transferred to a realistic portrait by changing its appearance style while maintaining the facial identity. To this end, we collect a new dataset, Daz-Rendered-Faces-HQ (DRFHQ), that is specifically designed for rendering-style portraits. We leverage this dataset to fine-tune the StyleGAN2 generator, using our carefully crafted framework, which helps to preserve the geometric and color features relevant to facial identity. We evaluate our framework using portraits with diverse gender, age, and race variations. Qualitative and quantitative evaluations and ablation studies show the advantages of our method compared to state-of-the-art approaches.Comment: 10 pages, 8 figures, 2 table

EyelashNet: A Dataset and A Baseline Method for Eyelash Matting

Eyelashes play a crucial part in the human facial structure and largely affect the facial attractiveness in modern cosmetic design. However, the appearance and structure of eyelashes can easily induce severe artifacts in high-fidelity multi-view 3D face reconstruction. Unfortunately it is highly challenging to remove eyelashes from portrait images using both traditional and learning-based matting methods due to the delicate nature of eyelashes and the lack of eyelash matting dataset. To this end, we present EyelashNet, the first eyelash matting dataset which contains 5,400 high-quality eyelash matting data captured from real world and 5,272 virtual eyelash matting data created by rendering avatars. Our work consists of a capture stage and an inference stage to automatically capture and annotate eyelashes instead of tedious manual efforts. The capture is based on a specifically-designed fluorescent labeling system. By coloring the eyelashes with a safe and invisible fluorescent substance, our system takes paired photos with colored and normal eyelashes by turning the equipped ultraviolet (UVA) flash on and off. We further correct the alignment between each pair of photos and use a novel alpha matte inference network to extract the eyelash alpha matte. As there is no prior eyelash dataset, we propose a progressive training strategy that progressively fuses captured eyelash data with virtual eyelash data to learn the latent semantics of real eyelashes. As a result, our method can accurately extract eyelash alpha mattes from fuzzy and self-shadow regions such as pupils, which is almost impossible by manual annotations. To validate the advantage of EyelashNet, we present a baseline method based on deep learning that achieves state-of-the-art eyelash matting performance with RGB portrait images as input. We also demonstrate that our work can largely benefit important real applications including high-fidelity personalized avatar and cosmetic design

Development of Catalytic Combustion and CO\u3csub\u3e2\u3c/sub\u3e Capture and Conversion Technology

Changes are needed to improve the efficiency and lower the CO2 emissions of traditional coal-fired power generation, which is the main source of global CO2 emissions. The integrated gasification fuel cell (IGFC) process, which combines coal gasification and high-temperature fuel cells, was proposed in 2017 to improve the efficiency of coal-based power generation and reduce CO2 emissions. Supported by the National Key R&D Program of China, the IGFC for nearzero CO2 emissions program was enacted with the goal of achieving near-zero CO2 emissions based on (1) catalytic combustion of the flue gas from solid oxide fuel cell (SOFC) stacks and (2) CO2 conversion using solid oxide electrolysis cells (SOECs). In this work, we investigated a kW-level catalytic combustion burner and SOEC stack, evaluated the electrochemical performance of the SOEC stack in H2O electrolysis and H2O/CO2 co-electrolysis, and established a multiscale and multi-physical coupling simulation model of SOFCs and SOECs. The process developed in this work paves the way for the demonstration and deployment of IGFC technology in the future

The clinical effectiveness of staple line reinforcement with different matrix used in surgery

Staplers are widely used in clinics; however, complications such as bleeding and leakage remain a challenge for surgeons. To tackle this issue, buttress materials are recommended to reinforce the staple line. This Review provides a systematic summary of the characteristics and applications of the buttress materials. First, the physical and chemical properties of synthetic polymer materials and extracellular matrix used for the buttress materials are introduced, as well as their pros and cons in clinical applications. Second, we review the clinical effects of reinforcement mesh in pneumonectomy, sleeve gastrectomy, pancreatectomy, and colorectal resection. Based on the analysis of numerous research data, we believe that buttress materials play a crucial role in increasing staple line strength and reducing the probability of complications, such as bleeding and leakage. However, considering the requirements of bioactivity, degradability, and biosafety, non-crosslinked small intestinal submucosa (SIS) matrix material is the preferred candidate. It has high research and application value, but further studies are required to confirm this. The aim of this Review is to provide comprehensive guidance on the selection of materials for staple line reinforcement

Simultaneous arthroscopic cystectomy and unicompartmental knee arthroplasty for the management of partial knee osteoarthritis with a popliteal cyst: A case report

IntroductionPopliteal cysts are secondary to degenerative changes in the knee joint. After total knee arthroplasty (TKA), 56.7% of patients with popliteal cysts at 4.9 years follow-up remained symptomatic in the popliteal area. However, the result of simultaneous arthroscopic cystectomy and unicompartmental knee arthroplasty (UKA) was uncertain.Case presentationA 57-year-old man was admitted to our hospital with severe pain and swelling in his left knee and the popliteal area. He was diagnosed with severe medial unicompartmental knee osteoarthritis (KOA) with a symptomatic popliteal cyst. Subsequently, arthroscopic cystectomy and unicompartmental knee arthroplasty (UKA) were performed simultaneously. A month after the operation, he returned to his normal life. There was no progression in the lateral compartment of the left knee and no recurrence of the popliteal cyst at the 1-year follow-up.ConclusionFor KOA patients with a popliteal cyst seeking UKA, simultaneous arthroscopic cystectomy and UKA are feasible with great success if managed appropriately

Synthesis and Growth Mechanism of Ni Nanotubes and Nanowires

Highly ordered Ni nanotube and nanowire arrays were fabricated via electrodeposition. The Ni microstructures and the process of the formation were investigated using conventional and high-resolution transmission electron microscope. Herein, we demonstrated the systematic fabrication of Ni nanotube and nanowire arrays and proposed an original growth mechanism. With the different deposition time, nanotubes or nanowires can be obtained. Tubular nanostructures can be obtained at short time, while nanowires take longer time to form. This formation mechanism is applicable to design and synthesize other metal nanostructures and even compound nanostuctures via template-based electrodeposition