DeformToon3D: Deformable 3D Toonification from Neural Radiance Fields

In this paper, we address the challenging problem of 3D toonification, which involves transferring the style of an artistic domain onto a target 3D face with stylized geometry and texture. Although fine-tuning a pre-trained 3D GAN on the artistic domain can produce reasonable performance, this strategy has limitations in the 3D domain. In particular, fine-tuning can deteriorate the original GAN latent space, which affects subsequent semantic editing, and requires independent optimization and storage for each new style, limiting flexibility and efficient deployment. To overcome these challenges, we propose DeformToon3D, an effective toonification framework tailored for hierarchical 3D GAN. Our approach decomposes 3D toonification into subproblems of geometry and texture stylization to better preserve the original latent space. Specifically, we devise a novel StyleField that predicts conditional 3D deformation to align a real-space NeRF to the style space for geometry stylization. Thanks to the StyleField formulation, which already handles geometry stylization well, texture stylization can be achieved conveniently via adaptive style mixing that injects information of the artistic domain into the decoder of the pre-trained 3D GAN. Due to the unique design, our method enables flexible style degree control and shape-texture-specific style swap. Furthermore, we achieve efficient training without any real-world 2D-3D training pairs but proxy samples synthesized from off-the-shelf 2D toonification models.Comment: ICCV 2023. Code: https://github.com/junzhezhang/DeformToon3D Project page: https://www.mmlab-ntu.com/project/deformtoon3d

Molecular mechanisms through which different carbon sources affect denitrification by Thauera linaloolentis: Electron generation, transfer, and competition

Characterizing the molecular mechanism through which different carbon sources affect the denitrification process would provide a basis for the proper selection of carbon sources, thus avoiding excessive carbon source dosing and secondary pollution while also improving denitrification efficiency. Here, we selected Thauera linaloolentis as a model organism of denitrification, whose genomic information was elucidated by draft genome sequencing and KEGG annotations, to investigate the growth kinetics, denitrification performances and characteristics of metabolic pathways under diverse carbon source conditions. We reconstructed a metabolic network of Thauera linaloolentis based on genomic analysis to help develop a systematic method of researching electron pathways. Our findings indicated that carbon sources with simple metabolic pathways (e.g., ethanol and sodium acetate) promoted the reproduction of Thauera linaloolentis, and its maximum growth density reached OD600 = 0.36 and maximum specific growth rate reached 0.145 h−1. These carbon sources also accelerated the denitrification process without the accumulation of intermediates. Nitrate could be reduced completely under any carbon source condition; but in the “glucose group”, the maximum accumulation of nitrite was 117.00 mg/L (1.51 times more than that in the “ethanol group”, which was 77.41 mg/L), the maximum accumulation of nitric oxide was 363.02 μg/L (7.35 times more than that in the “ethanol group”, which was 49.40 μg/L), and the maximum accumulation of nitrous oxide was 22.58 mg/L (26.56 times more than that in the “ethanol group”, which was 0.85 mg/L). Molecular biological analyses demonstrated that diverse types of carbon sources directly induced different carbon metabolic activities, resulting in variations in electron generation efficiency. Furthermore, the activities of the electron transport system were positively correlated with different carbon metabolic activities. Finally, these differences were reflected in the phenomenon of electronic competition between denitrifying reductases. Thus we concluded that this was the main molecular mechanism through which the carbon source type affected the denitrification process. In brief, carbon sources with simple metabolic pathways induced higher efficiency of electron generation, transfer, and competition, which promoted rapid proliferation and complete denitrification; otherwise Thauera linaloolentis would grow slowly and intermediate products would accumulate seriously. Our study established a method to evaluate and optimize carbon source utilization efficiency based on confirmed molecular mechanisms

TGLO Establish Historical Long-term Wetland Boundary Evolution Through Satellite Imagery

<p>This dataset includes wetland erosion rates for West Galveston, Matagorda, and San Antonio Bay. Long-term trends in wetland boundary changes are estimated using Landsat satellite imagery. Sections of the wetland experiencing the highest rates of erosion will be further investigated through CubeSat satellite observations. The format of the data is summarized as follows</p><p>(1) Landsat based wetland evolution results from 1984 to 2020</p><ul><li>The Annual and seasonal water occurrence (in gif format)</li><li>The wetland change map (in TIF format)</li></ul><p>(2) CubeSat based wetland evolution results from 2009 to 2020</p><ul><li>Water occurrence maps from 2009 to 2021 for the RapidEye based bi-annual results (FSX-occurrence-yyy1-yyy2.tif) and the PlanetScope based annual results (FSX-occurrence-yyy1.tif), where yyyy represents the given year. The legend image is 'occurrence-cbar.jpg'</li><li>Erosion maps based on the difference between water occurrence mapping in 2009 and 2021: ('FSX-occurrence-diff-2021-2009.tif'). The legend is 'occurrence-diff-cbar.jpg'</li><li>The  0.2-meter bed counter line images based on the water occurrence maps and the tide elevation threshold from 2017 to 2021: (FSX-bed-yyy1.tif). Again, yyy1 represents the given year. The legend is 'color-bed.jpg</li><li>The difference between the beds in the 0.2-meter bed counter line images  in 2017 and 2021 at FS-1, FS-2, FS-3, and FS-4: (FSX-bed-diff-2021-2017.tif). The legend is 'color-bed-dif.jpg'</li></ul><p>(3) Analysis of wetland boundary evolution and erosion rate</p><ul><li>Landsat based Wetland erosion rate from 1984 to 2020 and CubeSat erosion rate from 2009 to 2021 (data format in ArcGIS shapefile)*</li><li>Landsat based coastlines in 1984, 2000, 2010, and 2020*</li><li>* The dataset also shown on the Coast Atlas website (<a href="https://www.texascoastalatlas.com/bayatlas/index.html">this link</a>)</li></ul><p> </p><p> </p><p> </p&gt