Fashion Matrix: Editing Photos by Just Talking

The utilization of Large Language Models (LLMs) for the construction of AI systems has garnered significant attention across diverse fields. The extension of LLMs to the domain of fashion holds substantial commercial potential but also inherent challenges due to the intricate semantic interactions in fashion-related generation. To address this issue, we developed a hierarchical AI system called Fashion Matrix dedicated to editing photos by just talking. This system facilitates diverse prompt-driven tasks, encompassing garment or accessory replacement, recoloring, addition, and removal. Specifically, Fashion Matrix employs LLM as its foundational support and engages in iterative interactions with users. It employs a range of Semantic Segmentation Models (e.g., Grounded-SAM, MattingAnything, etc.) to delineate the specific editing masks based on user instructions. Subsequently, Visual Foundation Models (e.g., Stable Diffusion, ControlNet, etc.) are leveraged to generate edited images from text prompts and masks, thereby facilitating the automation of fashion editing processes. Experiments demonstrate the outstanding ability of Fashion Matrix to explores the collaborative potential of functionally diverse pre-trained models in the domain of fashion editing.Comment: 13 pages, 5 figures, 2 table

Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN

Source at https://proceedings.neurips.cc/paper/2021/hash/151de84cca69258b17375e2f44239191-Abstract.html.Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential. Yet, as most try-on approaches fit in-shop garments onto a target person, they require the laborious and restrictive construction of a paired training dataset, severely limiting their scalability. While a few recent works attempt to transfer garments directly from one person to another, alleviating the need to collect paired datasets, their performance is impacted by the lack of paired (supervised) information. In particular, disentangling style and spatial information of the garment becomes a challenge, which existing methods either address by requiring auxiliary data or extensive online optimization procedures, thereby still inhibiting their scalability. To achieve a scalable virtual try-on system that can transfer arbitrary garments between a source and a target person in an unsupervised manner, we thus propose a texture-preserving end-to-end network, the PAtch-routed SpaTially-Adaptive GAN (PASTA-GAN), that facilitates real-world unpaired virtual try-on. Specifically, to disentangle the style and spatial information of each garment, PASTA-GAN consists of an innovative patch-routed disentanglement module for successfully retaining garment texture and shape characteristics. Guided by the source person's keypoints, the patch-routed disentanglement module first decouples garments into normalized patches, thus eliminating the inherent spatial information of the garment, and then reconstructs the normalized patches to the warped garment complying with the target person pose. Given the warped garment, PASTA-GAN further introduces novel spatially-adaptive residual blocks that guide the generator to synthesize more realistic garment details. Extensive comparisons with paired and unpaired approaches demonstrate the superiority of PASTA-GAN, highlighting its ability to generate high-quality try-on images when faced with a large variety of garments(e.g. vests, shirts, pants), taking a crucial step towards real-world scalable try-on

GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning

Image-based Virtual Try-ON aims to transfer an in-shop garment onto a specific person. Existing methods employ a global warping module to model the anisotropic deformation for different garment parts, which fails to preserve the semantic information of different parts when receiving challenging inputs (e.g, intricate human poses, difficult garments). Moreover, most of them directly warp the input garment to align with the boundary of the preserved region, which usually requires texture squeezing to meet the boundary shape constraint and thus leads to texture distortion. The above inferior performance hinders existing methods from real-world applications. To address these problems and take a step towards real-world virtual try-on, we propose a General-Purpose Virtual Try-ON framework, named GP-VTON, by developing an innovative Local-Flow Global-Parsing (LFGP) warping module and a Dynamic Gradient Truncation (DGT) training strategy. Specifically, compared with the previous global warping mechanism, LFGP employs local flows to warp garments parts individually, and assembles the local warped results via the global garment parsing, resulting in reasonable warped parts and a semantic-correct intact garment even with challenging inputs.On the other hand, our DGT training strategy dynamically truncates the gradient in the overlap area and the warped garment is no more required to meet the boundary constraint, which effectively avoids the texture squeezing problem. Furthermore, our GP-VTON can be easily extended to multi-category scenario and jointly trained by using data from different garment categories. Extensive experiments on two high-resolution benchmarks demonstrate our superiority over the existing state-of-the-art methods.Comment: 8 pages, 8 figures, The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR

Separation and detection of polar cuticular components from Oriental tobacco leaf by integration of normal-phase liquid chromatography fractionation with reversed-phase liquid chromatography-mass spectrometry

Integration of normal-phase LC (NPLC) fractionation with RPLC-ESI/MS was established to detect the polar fractions of cuticular components from Oriental tobacco leaf. NPLC was selected for the fractionation of polar components of cuticular leaf extract, after being concentrated with rotary evaporator, each of the enriched fractions was further analyzed by RPLC-ESI/MS. In total, 83 compounds were finally detected including 45 cembranoids, 15 labdanoids, 20 sucrose esters, and 3 glucose esters (or fructose esters). Three cembranoids and seven labdanoids possibly are new diterpenoids. Glucose esters (or fructose esters) are also reported in Nicotiana tobacco for the first time

Spatio-Temporal Characteristics of Landscape Ecological Risks in the Ecological Functional Zone of the Upper Yellow River, China

The Ecological Functional Zone of the Upper Yellow River (EFZUYR) is a critical water-catching area in the Yellow River Basin, the ecological security of which affects the sound development of the ecosystem in the entire basin. Recently, significant land use changes have aggravated regional ecological risks and seriously affected the sustainable development of EFZUYR. In this context, this paper provides an in-depth study of the ecological risks caused by land use landscape changes. With the help of land use data and dynamic degree analysis, the land use transfer matrix, and the landscape pattern index, this paper quantifies the distribution trends of land use landscape patterns in EFZUYR from 1990 to 2018. In addition, this research explores the temporal and spatial dynamic distribution characteristics of landscape ecological risks in this functional zone. The research results show the following: (1) The transfer of land use in EFZUYR from 1990 to 2018 mainly occurred among cultivated land, grassland, and woodland, with the transferred area accounting for 87.16% of the total changed area. (2) The fragmentation degree of built-up areas is 0.1097, 0.1053, 0.0811 and 0.0762 in 1990, 2000, 2010 and 2018, respectively, with a decreasing trend. The dominance degree of grassland has been maintained at the highest level for a long time, with all values above 0.59. The separation degree and the interference degree of built-up areas were the highest and the values of the four periods were above 1.2 and 0.44, respectively. The loss degree of water was the highest, with a value above 0.67, while the value of other land use was mostly below 0.4. (3) The landscape ecological risk of EFZUYR presented a fluctuating rising, falling, and then rising trend. The spatial distribution characteristic of EFZUYR presented “high in the north and south, low in the middle.”, which has been maintained for a long time. The proportion of low-risk areas is as high as 70%, and the overall ecological risk of the region was low. However, the ecological risk of some areas, such as Linxia City and Magu County, increased. These findings can provide theoretical support for land use planning and achieving sustainable development of EFZUYR

A novel SLC6A8 mutation associated with intellectual disabilities in a Chinese family exhibiting creatine transporter deficiency: case report

Abstract Background X-linked creatine transporter deficiency (OMIM#300036,CRTR-D) is characterized by cerebral creatine deficiency, intellectual disabilities, severe speech impairment, seizures and behavioral problems. Mutations in the creatine transporter gene SLC6A8, a member of the solute-carrier family 6 mapped to Xq28, have been reported to cause the creatine transporter deficiency. Case presentation The proband presented at 5 yrs. 1 month of age with delays in intellectual and development, seizures and behavioral problems. A novel missense mutation, c.1181C > A (p.Thr394Lys), in the SLC6A8 gene (NM_005629.3) was detected via targeted exome sequencing, and then validated by Sanger sequencing. Multiple in silico variant effect analysis methods, including SIFT, PolyPhen2, PROVEAN, and Mutation Taster predicted that this variant was likely damaging or diseasing-causing. This hemizygous variation was also identified in the affected brother with the same clinical condition and inherited from the heterozygous carrier mother. The diagnosis was suggested by increased urinary creatine/creatinine (Cr:Crn) ratio and markedly reduced creatine content peak by brain proton magnetic resonance spectroscopy (MRS). The proband’s mother became pregnant with a 3rd sibling, in whom the Sanger sequencing result of c.1181C > A was negative. Conclusion The novel mutation c.1181C > A in the SLC6A8 gene reported in a Chinese family has expanded the mutation spectrum of CRTR-D. The combination of powerful new technologies such as targeted exome sequencing with thorough systematic clinical evaluation of patients will improve the diagnostic yield, and assist in genetic counselling and prenatal diagnosis for suspected genetic disorders

De novo paternal origin duplication of chromosome 11p15.5: report of two Chinese cases with Beckwith-Wiedemann syndrome

Abstract Background The molecular etiology of Beckwith-Wiedemann syndrome (BWS) is complex and heterogeneous. Several subtypes of epigenetic-genetic alterations including aberrant methylation patterns, segmental uniparental disomy, single gene mutations, and copy number changes have been described. An integrated molecular approach to analyze the epigenetic-genetic alterations is required for accurate diagnosis of BWS. Case presentation We reported two Chinese cases with BWS detected by genome-wide copy number analysis and locus-specific methylation profiling. Prenatal analysis on cord blood of patient 1 showed a de novo paternal origin duplication spanning 896Kb at 11p15.5. Patient 2 was referred at 2-month old and the genetic analysis showed a de novo 228.8Kb deletion at 11p15.5 telomeric end and a de novo duplication of 2.5 Mb at 11p15.5–15.4. Both the duplications are of paternal origin with gain of methylation at the imprinting center 1 and thus belong to the subgroup of a low tumor risk. Conclusion Results from these two cases and other reported cases from literature indicated that paternally derived duplications at 11p15.5 region cause BWS. Combined chromosome microarray analysis and methylation profiling provided reliable diagnosis for this subtype of BWS. Characterization of genetic defects in BWS patients could lead to better understanding the genetic mechanisms of this clinically and genetically heterogeneous disorder

Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN

Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential. Yet, as most try-on approaches fit in-shop garments onto a target person, they require the laborious and restrictive construction of a paired training dataset, severely limiting their scalability. While a few recent works attempt to transfer garments directly from one person to another, alleviating the need to collect paired datasets, their performance is impacted by the lack of paired (supervised) information. In particular, disentangling style and spatial information of the garment becomes a challenge, which existing methods either address by requiring auxiliary data or extensive online optimization procedures, thereby still inhibiting their scalability. To achieve a scalable virtual try-on system that can transfer arbitrary garments between a source and a target person in an unsupervised manner, we thus propose a texture-preserving end-to-end network, the PAtch-routed SpaTially-Adaptive GAN (PASTA-GAN), that facilitates real-world unpaired virtual try-on. Specifically, to disentangle the style and spatial information of each garment, PASTA-GAN consists of an innovative patch-routed disentanglement module for successfully retaining garment texture and shape characteristics. Guided by the source person's keypoints, the patch-routed disentanglement module first decouples garments into normalized patches, thus eliminating the inherent spatial information of the garment, and then reconstructs the normalized patches to the warped garment complying with the target person pose. Given the warped garment, PASTA-GAN further introduces novel spatially-adaptive residual blocks that guide the generator to synthesize more realistic garment details. Extensive comparisons with paired and unpaired approaches demonstrate the superiority of PASTA-GAN, highlighting its ability to generate high-quality try-on images when faced with a large variety of garments(e.g. vests, shirts, pants), taking a crucial step towards real-world scalable try-on