HumanRef: Single Image to 3D Human Generation via Reference-Guided Diffusion

Generating a 3D human model from a single reference image is challenging because it requires inferring textures and geometries in invisible views while maintaining consistency with the reference image. Previous methods utilizing 3D generative models are limited by the availability of 3D training data. Optimization-based methods that lift text-to-image diffusion models to 3D generation often fail to preserve the texture details of the reference image, resulting in inconsistent appearances in different views. In this paper, we propose HumanRef, a 3D human generation framework from a single-view input. To ensure the generated 3D model is photorealistic and consistent with the input image, HumanRef introduces a novel method called reference-guided score distillation sampling (Ref-SDS), which effectively incorporates image guidance into the generation process. Furthermore, we introduce region-aware attention to Ref-SDS, ensuring accurate correspondence between different body regions. Experimental results demonstrate that HumanRef outperforms state-of-the-art methods in generating 3D clothed humans with fine geometry, photorealistic textures, and view-consistent appearances.Comment: Homepage: https://eckertzhang.github.io/HumanRef.github.io

SAMPLING: Scene-adaptive Hierarchical Multiplane Images Representation for Novel View Synthesis from a Single Image

Recent novel view synthesis methods obtain promising results for relatively small scenes, e.g., indoor environments and scenes with a few objects, but tend to fail for unbounded outdoor scenes with a single image as input. In this paper, we introduce SAMPLING, a Scene-adaptive Hierarchical Multiplane Images Representation for Novel View Synthesis from a Single Image based on improved multiplane images (MPI). Observing that depth distribution varies significantly for unbounded outdoor scenes, we employ an adaptive-bins strategy for MPI to arrange planes in accordance with each scene image. To represent intricate geometry and multi-scale details, we further introduce a hierarchical refinement branch, which results in high-quality synthesized novel views. Our method demonstrates considerable performance gains in synthesizing large-scale unbounded outdoor scenes using a single image on the KITTI dataset and generalizes well to the unseen Tanks and Temples dataset.The code and models will soon be made available

ConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis

In this work, we propose a method to address the challenge of rendering a 3D human from a single image in a free-view manner. Some existing approaches could achieve this by using generalizable pixel-aligned implicit fields to reconstruct a textured mesh of a human or by employing a 2D diffusion model as guidance with the Score Distillation Sampling (SDS) method, to lift the 2D image into 3D space. However, a generalizable implicit field often results in an over-smooth texture field, while the SDS method tends to lead to a texture-inconsistent novel view with the input image. In this paper, we introduce a texture-consistent back view synthesis module that could transfer the reference image content to the back view through depth and text-guided attention injection. Moreover, to alleviate the color distortion that occurs in the side region, we propose a visibility-aware patch consistency regularization for texture mapping and refinement combined with the synthesized back view texture. With the above techniques, we could achieve high-fidelity and texture-consistent human rendering from a single image. Experiments conducted on both real and synthetic data demonstrate the effectiveness of our method and show that our approach outperforms previous baseline methods.Comment: see project page: https://gaoxiangjun.github.io/contex_human

Low-carbon developments in Northeast China: Evidence from cities

Cities are a major source of energy use and greenhouse gases emissions, as well as being at the core of the climate change mitigation. With the Revitalizing Old Industrial Base of Northeast China strategy, Northeast China has been a typical developing region with rapid industrialization and urbanization accompanied by substantial energy consumption and carbon emissions. Therefore, northeastern Chinese cities should play an important role in regional low-carbon developments. This study presents several improvements to previous method to improve the accuracy of the results. Using the modified method, for the first time, we compile carbon emission inventories for 30 cities in Northeast China based on fossil fuel combustion and industrial processes. The results indicate that Anshan emitted the most carbon emissions annually, followed by Benxi and the vice-provincial cities (including Changchun, Shenyang, Dalian and Harbin). In 2012, the total carbon emissions of the 30 cities amounted to 973.95 million tonnes, accounting for 9.71% and 2.75% of national and global carbon emissions, respectively. Most of the CO2 emissions of these cities were from the ‘nonmetal and metal industry’ and ‘energy production and supply’. Raw coal was the primary source of carbon emissions in Northeast China, and industrial processes also played a significant role in determining the carbon emissions. Additionally, both the average per capita carbon emissions and carbon emission intensity in the 30 cities were higher than the national levels. According to the differences in carbon emissions characteristics, we present several policy recommendations for carbon mitigation for northeastern Chinese cities. This study provides consistent and comparable spatial-temporal city-level emission database for further research on relationships between economic development and environmental protection in Northeast China. Simultaneously, this study provides practical reference values for other developing regions throughout the world to create low-carbon road maps

NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination

Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named neural ambient illumination (NeAI) that uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convoluted background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes. The project and supplementary materials are available at https://yiyuzhuang.github.io/NeAI/.Comment: Project page: <a class="link-external link-https" href="https://yiyuzhuang.github.io/NeAI/" rel="external noopener nofollow">https://yiyuzhuang.github.io/NeAI/</a

HiFi-123: Towards High-fidelity One Image to 3D Content Generation

Recent advances in text-to-image diffusion models have enabled 3D generation from a single image. However, current image-to-3D methods often produce suboptimal results for novel views, with blurred textures and deviations from the reference image, limiting their practical applications. In this paper, we introduce HiFi-123, a method designed for high-fidelity and multi-view consistent 3D generation. Our contributions are twofold: First, we propose a reference-guided novel view enhancement technique that substantially reduces the quality gap between synthesized and reference views. Second, capitalizing on the novel view enhancement, we present a novel reference-guided state distillation loss. When incorporated into the optimization-based image-to-3D pipeline, our method significantly improves 3D generation quality, achieving state-of-the-art performance. Comprehensive evaluations demonstrate the effectiveness of our approach over existing methods, both qualitatively and quantitatively

3D GAN Inversion with Facial Symmetry Prior

Recently, a surge of high-quality 3D-aware GANs have been proposed, which leverage the generative power of neural rendering. It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, referred as 3D GAN inversion. Although with the facial prior preserved in pre-trained 3D GANs, reconstructing a 3D portrait with only one monocular image is still an ill-pose problem. The straightforward application of 2D GAN inversion methods focuses on texture similarity only while ignoring the correctness of 3D geometry shapes. It may raise geometry collapse effects, especially when reconstructing a side face under an extreme pose. Besides, the synthetic results in novel views are prone to be blurry. In this work, we propose a novel method to promote 3D GAN inversion by introducing facial symmetry prior. We design a pipeline and constraints to make full use of the pseudo auxiliary view obtained via image flipping, which helps obtain a robust and reasonable geometry shape during the inversion process. To enhance texture fidelity in unobserved viewpoints, pseudo labels from depth-guided 3D warping can provide extra supervision. We design constraints aimed at filtering out conflict areas for optimization in asymmetric situations. Comprehensive quantitative and qualitative evaluations on image reconstruction and editing demonstrate the superiority of our method.Comment: Project Page is at https://feiiyin.github.io/SPI

Design of Reflective Intensity Modulated Fiber-Optic Sensor Based on TracePro and Taguchi Method

Abstract: Compare with traditional way of numerical simulation by establishing the mathematical model through geometry optic, we design a TracePro model to analyze the sensing process of reflective intensitymodulated fiber optic sensor base on ray tracing. This type of sensor has advantages over other fiber optic sensor, including simple structure, flexible design, reliable perform, low cost etc. In this paper, to design the reflective intensity modulated fiber optic sensor with concave reflected surface, TracePro software is used for modeling, TP modeling results are consistent with the existing conclusions show that the method is reasonably effectively, can improve the design efficiency. Meanwhile the Taguchi method is used to optimize coupling efficiency of receiving fiber in fiber optic displacement sensor design. Through optimizing three controllable factors the optimization configuration of A1B1C1 combinations is gain, presents a viable solution for the design of this sensor type