One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization

Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a neural radiance field under the guidance of 2D diffusion models but suffer from lengthy optimization time, 3D inconsistency results, and poor geometry. In this work, we propose a novel method that takes a single image of any object as input and generates a full 360-degree 3D textured mesh in a single feed-forward pass. Given a single image, we first use a view-conditioned 2D diffusion model, Zero123, to generate multi-view images for the input view, and then aim to lift them up to 3D space. Since traditional reconstruction methods struggle with inconsistent multi-view predictions, we build our 3D reconstruction module upon an SDF-based generalizable neural surface reconstruction method and propose several critical training strategies to enable the reconstruction of 360-degree meshes. Without costly optimizations, our method reconstructs 3D shapes in significantly less time than existing methods. Moreover, our method favors better geometry, generates more 3D consistent results, and adheres more closely to the input image. We evaluate our approach on both synthetic data and in-the-wild images and demonstrate its superiority in terms of both mesh quality and runtime. In addition, our approach can seamlessly support the text-to-3D task by integrating with off-the-shelf text-to-image diffusion models.Comment: project website: one-2-3-45.co

Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view. To take full advantage of pretrained 2D generative priors, we develop various conditioning and training schemes to minimize the effort of finetuning from off-the-shelf image diffusion models such as Stable Diffusion. Zero123++ excels in producing high-quality, consistent multi-view images from a single image, overcoming common issues like texture degradation and geometric misalignment. Furthermore, we showcase the feasibility of training a ControlNet on Zero123++ for enhanced control over the generation process. The code is available at https://github.com/SUDO-AI-3D/zero123plus

OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects

We introduce OpenIllumination, a real-world dataset containing over 108K images of 64 objects with diverse materials, captured under 72 camera views and a large number of different illuminations. For each image in the dataset, we provide accurate camera parameters, illumination ground truth, and foreground segmentation masks. Our dataset enables the quantitative evaluation of most inverse rendering and material decomposition methods for real objects. We examine several state-of-the-art inverse rendering methods on our dataset and compare their performances. The dataset and code can be found on the project page: https://oppo-us-research.github.io/OpenIllumination

One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion

Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts. However, most existing models fall short in simultaneously providing rapid generation speeds and high fidelity to input images - two features essential for practical applications. In this paper, we present One-2-3-45++, an innovative method that transforms a single image into a detailed 3D textured mesh in approximately one minute. Our approach aims to fully harness the extensive knowledge embedded in 2D diffusion models and priors from valuable yet limited 3D data. This is achieved by initially finetuning a 2D diffusion model for consistent multi-view image generation, followed by elevating these images to 3D with the aid of multi-view conditioned 3D native diffusion models. Extensive experimental evaluations demonstrate that our method can produce high-quality, diverse 3D assets that closely mirror the original input image. Our project webpage: https://sudo-ai-3d.github.io/One2345plus_page

Axitinib targets cardiac fibrosis in pressure overload-induced heart failure through VEGFA-KDR pathway

BackgroundThere are no specific clinical medications that target cardiac fibrosis in heart failure (HF). Recent studies have shown that tyrosine kinase inhibitors (TKIs) may benefit fibrosis in various organs. However, there is limited research on their application in cardiac fibrosis. Axitinib, an FDA-approved tyrosine kinase inhibitor, was used to evaluate its effects on cardiac fibrosis and function in pressure overload-induced heart failure.MethodsTo build a pharmacological network, the pharmacological targets of axitinib were first retrieved from databases and coupled with key heart failure gene molecules for analysis and prediction. To validate the results outlined above, 8-week-old male C57BL/6 J mice were orally administrated of axitinib (30 mg/kg) daily for 8 weeks after Transverse Aortic Constriction (TAC) surgery. Mouse cardiomyocytes and cardiac fibroblasts were used as cell lines to test the function and mechanism of axitinib.ResultsWe found that the pharmacological targets of axitinib could form a pharmacological network with key genes involved in heart failure. The VEGFA-KDR pathway was found to be closely related to the differential gene expression of human heart-derived primary cardiomyocyte cell lines treated with axitinib, based on analysis of the publicly available dataset. The outcomes of animal experiments demonstrated that axitinib therapy greatly reduced cardiac fibrosis and improved TAC-induced cardiac dysfunction. Further research has shown that the expression of transforming growth factor-β(TGF-β) and other fibrosis genes was significantly reduced in vivo and in vitro.ConclusionOur study provides evidence for the repurposing of axitinib to combat cardiac fibrosis, and offers new insights into the treatment of patients with HF

Rhizome Severing Increases Root Lifespan of Leymus chinensis in a Typical Steppe of Inner Mongolia

Root lifespan is an important trait that determines plants' ability to acquire and conserve soil resources. There have been several studies investigating characteristics of root lifespan of both woody and herbaceous species. However, most of the studies have focused on non-clonal plants, and there have been little data on root lifespan for clonal plants that occur widely in temperate grasslands.We investigated the effects of rhizome severing on overall root lifespan of Leymus chinensis, a clonal, dominant grass species in the temperate steppe in northern China, in a 2-year field study using modified rhizotron technique. More specifically, we investigated the effects of rhizome severing on root lifespan of roots born in different seasons and distributed at different soil depths. Rhizome severing led to an increase in the overall root lifespan from 81 to 103 days. The increase in root lifespan exhibited spatial and temporal characteristics such that it increased lifespan for roots distributed in the top two soil layers and for roots born in summer and spring, but it had no effect on lifespan of roots in the deep soil layer and born in autumn. We also examined the effect of rhizome severing on carbohydrate and N contents in roots, and found that root carbohydrate and N contents were not affected by rhizome severing. Further, we found that root lifespan of Stipa krylovii and Artemisia frigida, two dominant, non-clonal species in the temperate steppe, was significantly longer (118 d) than that of L. chinensis (81 d), and this value became comparable to that of L. chinensis under rhizome severing (103 d).We found that root lifespan in dominant, clonal L. chinensis was shorter than for the dominant, non-clonal species of S. krylovii and A. frigida. There was a substantial increase in the root lifespan of L. chinensis in response to severing their rhizomes, and this increase in root lifespan exhibited temporal and spatial characteristics. These findings suggest that the presence of rhizomes is likely to account for the observed short lifespan of clonal plant species in the temperate steppe

An Improved Evaluation Methodology for Mining Association Rules

At present, association rules have been widely used in prediction, personalized recommendation, risk analysis and other fields. However, it has been pointed out that the traditional framework to evaluate association rules, based on Support and Confidence as measures of importance and accuracy, has several drawbacks. Some papers presented several new evaluation methods; the most typical methods are Lift, Improvement, Validity, Conviction, Chi-square analysis, etc. Here, this paper first analyzes the advantages and disadvantages of common measurement indicators of association rules and then puts forward four new measure indicators (i.e., Bi-support, Bi-lift, Bi-improvement, and Bi-confidence) based on the analysis. At last, this paper proposes a novel Bi-directional interestingness measure framework to improve the traditional one. In conclusion, the bi-directional interestingness measure framework (Bi-support and Bi-confidence framework) is superior to the traditional ones in the aspects of the objective criterion, comprehensive definition, and practical application

An Improved Evaluation Methodology for Mining Association Rules

At present, association rules have been widely used in prediction, personalized recommendation, risk analysis and other fields. However, it has been pointed out that the traditional framework to evaluate association rules, based on Support and Confidence as measures of importance and accuracy, has several drawbacks. Some papers presented several new evaluation methods; the most typical methods are Lift, Improvement, Validity, Conviction, Chi-square analysis, etc. Here, this paper first analyzes the advantages and disadvantages of common measurement indicators of association rules and then puts forward four new measure indicators (i.e., Bi-support, Bi-lift, Bi-improvement, and Bi-confidence) based on the analysis. At last, this paper proposes a novel Bi-directional interestingness measure framework to improve the traditional one. In conclusion, the bi-directional interestingness measure framework (Bi-support and Bi-confidence framework) is superior to the traditional ones in the aspects of the objective criterion, comprehensive definition, and practical application