40 research outputs found
Coarse-to-Fine: Facial Structure Editing of Portrait Images via Latent Space Classifications
Facial structure editing of portrait images is challenging given the facial variety, the lack of ground-truth, the necessity of jointly adjusting color and shape, and the requirement of no visual artifacts. In this paper, we investigate how to perform chin editing as a case study of editing facial structures. We present a novel method that can automatically remove the double chin effect in portrait images. Our core idea is to train a fine classification boundary in the latent space of the portrait images. This can be used to edit the chin appearance by manipulating the latent code of the input portrait image while preserving the original portrait features. To achieve such a fine separation boundary, we employ a carefully designed training stage based on latent codes of paired synthetic images with and without a double chin. In the testing stage, our method can automatically handle portrait images with only a refinement to subtle misalignment before and after double chin editing. Our model enables alteration to the neck region of the input portrait image while keeping other regions unchanged, and guarantees the rationality of neck structure and the consistency of facial characteristics. To the best of our knowledge, this presents the first effort towards an effective application for editing double chins. We validate the efficacy and efficiency of our approach through extensive experiments and user studies
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