JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling

We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). JointNet is extended from a pre-trained text-to-image diffusion model, where a copy of the original network is created for the new dense modality branch and is densely connected with the RGB branch. The RGB branch is locked during network fine-tuning, which enables efficient learning of the new modality distribution while maintaining the strong generalization ability of the large-scale pre-trained diffusion model. We demonstrate the effectiveness of JointNet by using RGBD diffusion as an example and through extensive experiments, showcasing its applicability in a variety of applications, including joint RGBD generation, dense depth prediction, depth-conditioned image generation, and coherent tile-based 3D panorama generation

Solid state synthesis, sintering and dielectric properties of Li2MnSiO4 ceramics

The solid state reaction method was used to synthesise the Li2MnSiO4 ceramic. The first principle calculation, X-ray diffraction, scanning electron microscopy, network analysis, and differentia-thermal analysis were taken to analyse its sintering and dielectric properties. The TE-mode Cylindrical Cavity method based on a cavity resonator was conducted to obtain the dielectric property at different frequencies. Two phases were formed, namely, Li2SiO3 and LiMn2O4, and the increasing sintering temperature increases the amount of LiMn2O4 and decreases that of Li2SiO3. The peak dielectric properties were obtained at 1000 °C; εr = 7.99 at 9.2 GHz, 7.86 at 10.6 GHz and 7.8 at 14.2 GHz; and tanδ = 0.00375 at 9.2 GHz, 0.00387 at 10.6 GHz and 0.004 at 14.2 GHz. The bulk density is 3.44 g/cm3, and the relative density is 96.1%

RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs

We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR). While tremendous efforts have been devoted to parametric SVFR, a visible gap still lies between the result 3D shape and the ground truth. We believe there are two major obstacles: 1) the representation of the parametric model is limited to a certain face database; 2) 2D images and 3D shapes in the fitted datasets are distinctly misaligned. To resolve these issues, a large-scale pseudo 2D&3D dataset is created by first rendering the detailed 3D faces, then swapping the face in the wild images with the rendered face. These pseudo 2D&3D pairs are created from publicly available datasets which eliminate the gaps between 2D and 3D data while covering diverse appearances, poses, scenes, and illumination. We further propose a non-parametric scheme to learn a well-generalized SVFR model from the created dataset, and the proposed hierarchical signed distance function turns out to be effective in predicting middle-scale and small-scale 3D facial geometry. Our model outperforms previous methods on FaceScape-wild/lab and MICC benchmarks and is well generalized to various appearances, poses, expressions, and in-the-wild environments. The code is released at https://github.com/zhuhao-nju/rafare

High-Fidelity 3D Face Generation from Natural Language Descriptions

Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence. However, little research ever tapped into this task. We argue the major obstacle lies in 1) the lack of high-quality 3D face data with descriptive text annotation, and 2) the complex mapping relationship between descriptive language space and shape/appearance space. To solve these problems, we build Describe3D dataset, the first large-scale dataset with fine-grained text descriptions for text-to-3D face generation task. Then we propose a two-stage framework to first generate a 3D face that matches the concrete descriptions, then optimize the parameters in the 3D shape and texture space with abstract description to refine the 3D face model. Extensive experimental results show that our method can produce a faithful 3D face that conforms to the input descriptions with higher accuracy and quality than previous methods. The code and Describe3D dataset are released at https://github.com/zhuhao-nju/describe3d .Comment: Accepted to CVPR 202

Cu-Catalyzed 1,2-Dihydroisoquinolines Synthesis from <i>o</i>‑Ethynyl Benzacetals and Sulfonyl Azides

An efficient synthesis of 1,3-/1,1-dialkoxy 1,2-dihydroisoquinolines from <i>o</i>-ethynylbenzacetals and sulfonyl azides <i>via</i> a cascade process combining copper-catalyzed alkyne–azide cycloaddition (CuAAC), Dimroth rearrangement, 1,5-OR shift/1,5-H shift, and 6π-electrocyclic ring closure (6π-ERC) is described. Extension of the produced 1,3-dialkoxy-1,2-dihydroisoquinolines to isoquinolium salts is also disclosed

Cu-Catalyzed 1,2-Dihydroisoquinolines Synthesis from <i>o</i>‑Ethynyl Benzacetals and Sulfonyl Azides

An efficient synthesis of 1,3-/1,1-dialkoxy 1,2-dihydroisoquinolines from <i>o</i>-ethynylbenzacetals and sulfonyl azides <i>via</i> a cascade process combining copper-catalyzed alkyne–azide cycloaddition (CuAAC), Dimroth rearrangement, 1,5-OR shift/1,5-H shift, and 6π-electrocyclic ring closure (6π-ERC) is described. Extension of the produced 1,3-dialkoxy-1,2-dihydroisoquinolines to isoquinolium salts is also disclosed

Cu-Catalyzed 1,2-Dihydroisoquinolines Synthesis from <i>o</i>‑Ethynyl Benzacetals and Sulfonyl Azides

An efficient synthesis of 1,3-/1,1-dialkoxy 1,2-dihydroisoquinolines from <i>o</i>-ethynylbenzacetals and sulfonyl azides <i>via</i> a cascade process combining copper-catalyzed alkyne–azide cycloaddition (CuAAC), Dimroth rearrangement, 1,5-OR shift/1,5-H shift, and 6π-electrocyclic ring closure (6π-ERC) is described. Extension of the produced 1,3-dialkoxy-1,2-dihydroisoquinolines to isoquinolium salts is also disclosed

Cu-Catalyzed 1,2-Dihydroisoquinolines Synthesis from <i>o</i>‑Ethynyl Benzacetals and Sulfonyl Azides

An efficient synthesis of 1,3-/1,1-dialkoxy 1,2-dihydroisoquinolines from <i>o</i>-ethynylbenzacetals and sulfonyl azides <i>via</i> a cascade process combining copper-catalyzed alkyne–azide cycloaddition (CuAAC), Dimroth rearrangement, 1,5-OR shift/1,5-H shift, and 6π-electrocyclic ring closure (6π-ERC) is described. Extension of the produced 1,3-dialkoxy-1,2-dihydroisoquinolines to isoquinolium salts is also disclosed

3‑Alkenylation or 3‑Alkylation of Indole with Propargylic Alcohols: Construction of 3,4-Dihydrocyclopenta[<i>b</i>]indole and 1,4-Dihydrocyclopenta[<i>b</i>]indole in the Presence of Different Catalysts

3-Alkenylation or 3-alkylation of indole with propargylic alcohols could be efficiently controlled by the catalyst. In the presence of triflic acid, 3-alkenylation of indole occurred and a 3,4-dihydrocyclopenta­[<i>b</i>]­indole skeleton was effectively constructed in moderate to excellent yields via a cascade process. In the presence of Cu­(OTf)_2, 3-alkylation of indole resulted in the formation of 3-propargylic indole, which could be further converted into 2-iodo-1,4-dihydrocyclopenta­[<i>b</i>]­indoles in the presence of <i>N</i>-iodosuccinimide and boron trifluoride etherate. Possible mechanisms related to the 3-alkenylation or 3-alkylation of indole and their extension to the formation of 3,4-dihydrocyclopenta­[<i>b</i>]­indoles or 1,4-dihydrocyclopenta­[<i>b</i>]­indoles are postulated and discussed