SHERF: Generalizable Human NeRF from a Single Image

Existing Human NeRF methods for reconstructing 3D humans typically rely on multiple 2D images from multi-view cameras or monocular videos captured from fixed camera views. However, in real-world scenarios, human images are often captured from random camera angles, presenting challenges for high-quality 3D human reconstruction. In this paper, we propose SHERF, the first generalizable Human NeRF model for recovering animatable 3D humans from a single input image. SHERF extracts and encodes 3D human representations in canonical space, enabling rendering and animation from free views and poses. To achieve high-fidelity novel view and pose synthesis, the encoded 3D human representations should capture both global appearance and local fine-grained textures. To this end, we propose a bank of 3D-aware hierarchical features, including global, point-level, and pixel-aligned features, to facilitate informative encoding. Global features enhance the information extracted from the single input image and complement the information missing from the partial 2D observation. Point-level features provide strong clues of 3D human structure, while pixel-aligned features preserve more fine-grained details. To effectively integrate the 3D-aware hierarchical feature bank, we design a feature fusion transformer. Extensive experiments on THuman, RenderPeople, ZJU_MoCap, and HuMMan datasets demonstrate that SHERF achieves state-of-the-art performance, with better generalizability for novel view and pose synthesis.Comment: Accepted by ICCV2023. Project webpage: https://skhu101.github.io/SHERF

OCC-VO: Dense Mapping via 3D Occupancy-Based Visual Odometry for Autonomous Driving

Visual Odometry (VO) plays a pivotal role in autonomous systems, with a principal challenge being the lack of depth information in camera images. This paper introduces OCC-VO, a novel framework that capitalizes on recent advances in deep learning to transform 2D camera images into 3D semantic occupancy, thereby circumventing the traditional need for concurrent estimation of ego poses and landmark locations. Within this framework, we utilize the TPV-Former to convert surround view cameras' images into 3D semantic occupancy. Addressing the challenges presented by this transformation, we have specifically tailored a pose estimation and mapping algorithm that incorporates Semantic Label Filter, Dynamic Object Filter, and finally, utilizes Voxel PFilter for maintaining a consistent global semantic map. Evaluations on the Occ3D-nuScenes not only showcase a 20.6% improvement in Success Ratio and a 29.6% enhancement in trajectory accuracy against ORB-SLAM3, but also emphasize our ability to construct a comprehensive map. Our implementation is open-sourced and available at: https://github.com/USTCLH/OCC-VO.Comment: 7pages, 3 figure

Femtosecond Laser Filamentation in Atmospheric Turbulence

The effects of turbulence intensity and turbulence region on the distribution of femtosecond laser filaments are experimentally elaborated. Through the ultrasonic signals emitted by the filaments, and it is observed that increasing turbulence intensity and expanding turbulence active region cause an increase in the start position of the filament, and a decrease in filament length, which can be well explained by the theoretical calculation. It is also observed that the random perturbation of the air refractive index caused by atmospheric turbulence expanded the spot size of the filament. Additionally, when turbulence intensity reaches , multiple filaments are formed. Furthermore, the standard deviation of the transverse displacement of filament is found to be proportional to the square root of turbulent structure constant under the experimental turbulence parameters in this paper. These results contribute to the study of femtosecond laser propagation mechanisms in complex atmospheric turbulence conditionsComment: 9 pages, 4 figure

Preparation of Copper Oxide/TiO2 Composite Films by Mechanical Ball Milling and Investigated Photocatalytic Activity

The Cu/Ti composite coatings were prepared by the mechanical ball milling, the CuO/TiO2 and Cu2O/TiO2 composite photocatalytic films were obtained by the subsequent oxidation process. The microstructure of the composite films was analyzed by X-ray Diffraction (XRD）and scanning election microscope (SEM). The photocatalytic activity was evaluated, the effects of ball milling time on the formation of the Cu/Ti coatings were investigated, and the effects of the oxidation temperature and oxidation atmosphere on microstructure and photocatalytic activity of the films were studied. The results illustrate that the ball milling time has significant effects on the formation of the coatings and the coatings are continuous and compact by ball milling for 15 h. The photocatalytic activity of the CuO/TiO2 composite films is increased first and then decreased with the oxidation temperature increases, and the photocatalytic activity is the best at 500 °C. The CuO/TiO2 composite films are obtained by the oxidation of Cu/Ti coatings at 500 °C for 15 h in the air, while the Cu2O/TiO2 composite films are oxidized in carbon atmosphere. Photocatalysis efficiency of the films is obviously enhanced with the help of the p-n junction heterostructure in the Cu2O/TiO2 composite films

Exogenous melatonin mediates radish (Raphanus sativus) and Alternaria brassicae interaction in a dose-dependent manner

Radish (Raphanus sativus L.) is an economically important vegetable worldwide, but its sustainable production and breeding are highly threatened by blight disease caused by Alternaria brassicae. Melatonin is an important growth regulator that can influence physiological activities in both plants and microbes and stimulate biotic stress resistance in plants. In this study, 0-1500 μM melatonin was exogenously applied to healthy radish seedlings, in vitro incubated A. brassicae, and diseased radish seedlings to determine the effects of melatonin on host, pathogen, and host-pathogen interaction. At sufficient concentrations (0-500 μM), melatonin enhanced growth and immunity of healthy radish seedlings by improving the function of organelles and promoting the biosynthesis of antioxidant enzymes, chitin, organic acid, and defense proteins. Interestingly, melatonin also improved colony growth, development, and virulence of A. brassicae. A strong dosage-dependent effect of melatonin was observed: 50-500 μM promoted host and pathogen vitality and resistance (500 μM was optimal) and 1500 μM inhibited these processes. Significantly less blight was observed on diseased seedlings treated with 500 μM melatonin, indicating that melatonin more strongly enhanced the growth and immunity of radish than it promoted the development and virulence of A. brassicae at this treatment concentration. These effects of MT were mediated by transcriptional changes of key genes as identified by RNA-seq, Dual RNA-seq, and qRT-PCR. The results from this work provide a theoretical basis for the application of melatonin to protect vegetable crops against pathogens

SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling

Synthetic data has emerged as a promising source for 3D human research as it offers low-cost access to large-scale human datasets. To advance the diversity and annotation quality of human models, we introduce a new synthetic dataset, SynBody, with three appealing features: 1) a clothed parametric human model that can generate a diverse range of subjects; 2) the layered human representation that naturally offers high-quality 3D annotations to support multiple tasks; 3) a scalable system for producing realistic data to facilitate real-world tasks. The dataset comprises 1.2M images with corresponding accurate 3D annotations, covering 10,000 human body models, 1,187 actions, and various viewpoints. The dataset includes two subsets for human pose and shape estimation as well as human neural rendering. Extensive experiments on SynBody indicate that it substantially enhances both SMPL and SMPL-X estimation. Furthermore, the incorporation of layered annotations offers a valuable training resource for investigating the Human Neural Radiance Fields (NeRF).Comment: Accepted by ICCV 2023. Project webpage: https://synbody.github.io