arXiv subjects

Chao He

Publications and source records attributed to Chao He.

At least 55 records · Page 3Linked to original sources

arXiv2025

CartoonAlive: Towards Expressive Live2D Modeling from Single Portraits

With the rapid advancement of large foundation models, AIGC, cloud rendering, and real-time motion capture technologies, digital humans are now capable of achieving synchronized facial expressions and body movements, engaging in intelligent dialogues driven by natural language, and enabling the fast creation of personalized avatars. While current mainstream approaches to digital humans primarily focus on 3D models and 2D video-based representations, interactive 2D cartoon-style digital humans have received relatively less attention. Compared to 3D digital humans that require complex modeling and high rendering costs, and 2D video-based solutions that lack flexibility and real-time interactivity, 2D cartoon-style Live2D models offer a more efficient and expressive alternative. By simulating 3D-like motion through layered segmentation without the need for traditional 3D modeling, Live2D enables dynamic and real-time manipulation. In this technical report, we present CartoonAlive, an innovative method for generating high-quality Live2D digital humans from a single input portrait image. CartoonAlive leverages the shape basis concept commonly used in 3D face modeling to construct facial blendshapes suitable for Live2D. It then infers the corresponding blendshape weights based on facial keypoints detected from the input image. This approach allows for the rapid generation of a highly expressive and visually accurate Live2D model that closely resembles the input portrait, within less than half a minute. Our work provides a practical and scalable solution for creating interactive 2D cartoon characters, opening new possibilities in digital content creation and virtual character animation. The project homepage is https://human3daigc.github.io/CartoonAlive_webpage/.

cs.CV↗

arXiv2025

A reconfigurable arbitrary retarder array as complex structured matter

Tuneable retarder arrays, such as spatially patterned liquid crystal devices, have given rise to impressive photonic functionality, fuelling diverse applications ranging from microscopy and holography to encryption and communications. Presently these solutions are limited by the controllable degrees of freedom of structured matter, hindering applications that demand photonic systems with high flexibility and reconfigurable topologies. Here we demonstrate a compound modulator that implements a synthetic tuneable arbitrary retarder array as virtual pixels derived by cascading low functionality tuneable devices, realising full dynamic control of its arbitrary elliptical axis geometry, retardance value, and induced phase. Our approach offers unprecedented functionality that is user-defined and possesses high flexibility, allowing our modulator to act as a new beam generator, analyser, and corrector, opening an exciting path to tuneable topologies of light and matter.

physics.optics↗

arXiv2025

Complex structured light generation using printed liquid crystal droplets

Inkjet-printed liquid crystal (LC) droplets exhibit an intricate spatially-varying birefringence due to their complex internal director configuration. While such anisotropy is often viewed as a drawback when LC droplets are used as microlenses, here we leverage this remarkable birefringence property to generate complex structured light. Through a selection of the alignment layer, and by varying the chiral pitch, we create three distinct droplet types with tailored intrinsic director configurations, each exhibiting a unique birefringence distribution for structured light beam generation. We show that these printed LC droplets can generate beams that exhibit skyrmionic structures carrying two units of orbital angular momentum, beams that contain azimuthal/radial polarized fields, and beams with polarization singularities. Our method enables new possibilities for using LC droplet technology to engineer sophisticated optical beam patterns.

physics.optics↗

arXiv2025

MambaHash: Visual State Space Deep Hashing Model for Large-Scale Image Retrieval

Deep image hashing aims to enable effective large-scale image retrieval by mapping the input images into simple binary hash codes through deep neural networks. More recently, Vision Mamba with linear time complexity has attracted extensive attention from researchers by achieving outstanding performance on various computer tasks. Nevertheless, the suitability of Mamba for large-scale image retrieval tasks still needs to be explored. Towards this end, we propose a visual state space hashing model, called MambaHash. Concretely, we propose a backbone network with stage-wise architecture, in which grouped Mamba operation is introduced to model local and global information by utilizing Mamba to perform multi-directional scanning along different groups of the channel. Subsequently, the proposed channel interaction attention module is used to enhance information communication across channels. Finally, we meticulously design an adaptive feature enhancement module to increase feature diversity and enhance the visual representation capability of the model. We have conducted comprehensive experiments on three widely used datasets: CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that compared with the state-of-the-art deep hashing methods, our proposed MambaHash has well efficiency and superior performance to effectively accomplish large-scale image retrieval tasks. Source code is available https://github.com/shuaichaochao/MambaHash.git

cs.CV↗

arXiv2025

How CO Affects the Composition of Titan's Tholins Generated with ECR Plasma

Titan's atmosphere possesses thick haze layers, but their formation mechanisms remain poorly understood, including the influence of oxygen-containing gas components on organic matter synthesis. As the most abundant oxygen-containing gas, the presence of CO has been found to exert a significant impact on the generation of oxygen-containing organic compounds. Therefore, investigating the influence of CO on the production and composition of Tholins through laboratory simulations, holds profound scientific significance in the context of Titan. The work presented here is an experimental simulation designed to evaluate the impact of CO on the atmospheric chemistry of Titan. To this end, CO was introduced into the standard N2/CH4 mixture at varying mixing ratios from 0.2% to 9%, and exposed to Electron Cyclotron Resonance (ECR) plasma to initiate photochemical reactions. Optical emission spectroscopy was employed for gas-phase in situ characterization, while infrared spectroscopy and high-resolution mass spectrometry were used to analyze the resulting solid products (tholins). Our results demonstrate that the addition of CO enriches the complexity of the chemical system. CO not only supplies oxygen to the system, but also enhances nitrogen's reactivity and incorporation, enhancing the number and quantity of the organic products.

physics.chem-ph↗

arXiv2025

Physically Plausible Vectorial Metrics for Polarization Information Analysis

The Mueller Matrix Polar Decomposition method decomposes a Mueller matrix into a diattenuator, a retarder, and a depolarizer. Among these elements, the retarder, which plays a key role in medical and material characterization, is modelled as a circular retarder followed by a linear retarder when using this approach. However, this model may not accurately reflect the actual structure of the retarder in certain cases, as many practical retarders do not have a layered structure or consist of multiple (unknown) layers. Misinterpretation, therefore, may occur when the actual structure differs from the model. Here we circumvent this limitation by proposing to use a physically plausible parameter set that includes the axis orientation angle $ϕ$, the degree of ellipticity $χ$, and the elliptical retardance $ρ$. By working with this set of parameters, an overall characterization of a retarder is provided, encompassing its full optical response without making any assumptions about the structure of the material. In this study, experiments were carried out on liquid crystalline samples to validate the feasibility of our approach, demonstrating that the physically plausible parameter set adopted provides a useful tool for a broader range of applications in both biomedical imaging and optical material analysis.

physics.optics↗

arXiv2025

Modulated differentiable STFT and balanced spectrum metric for freight train wheelset bearing cross-machine transfer monitoring under speed fluctuations

The service conditions of wheelset bearings has a direct impact on the safe operation of railway heavy haul freight trains as the key components. However, speed fluctuation of the trains and few fault samples are the two main problems that restrict the accuracy of bearing fault diagnosis. Therefore, a cross-machine transfer diagnosis (pyDSN) network coupled with interpretable modulated differentiable short-time Fourier transform (STFT) and physics-informed balanced spectrum quality metric is proposed to learn domain-invariant and discriminative features under time-varying speeds. Firstly, due to insufficiency in extracting extract frequency components of time-varying speed signals using fixed windows, a modulated differentiable STFT (MDSTFT) that is interpretable with STFT-informed theoretical support, is proposed to extract the robust time-frequency spectrum (TFS). During training process, multiple windows with different lengths dynamically change. Also, in addition to the classification metric and domain discrepancy metric, we creatively introduce a third kind of metric, referred to as the physics-informed metric, to enhance transferable TFS. A physics-informed balanced spectrum quality (BSQ) regularization loss is devised to guide an optimization direction for MDSTFT and model. With it, not only can model acquire high-quality TFS, but also a physics-restricted domain adaptation network can be also acquired, making it learn real-world physics knowledge, ultimately diminish the domain discrepancy across different datasets. The experiment is conducted in the scenario of migrating from the laboratory datasets to the freight train dataset, indicating that the hybrid-driven pyDSN outperforms existing methods and has practical value.

cs.LG↗

arXiv2025

Effects of Ultraviolet Radiation on Sub-Neptune Exoplanet Hazes Through Laboratory Experiments

Temperate sub-Neptune exoplanets could contain large inventories of water in various phases, such as water-worlds with water-rich atmospheres or even oceans. Both space-based and ground-based observations have shown that many exoplanets likely also contain photochemically-generated hazes. Haze particles are a key source of organic matter and may impact the evolution or origin of life. In addition, haze layers could provide a mechanism for lower-atmospheric shielding and ultimately atmospheric retention. Often orbiting close to M-dwarf stars, these planets receive large amounts of radiation, especially during flaring events, which may strip away their atmospheres. M-dwarf stars are known to have higher stellar activity than other types of stars, and stellar flares have the potential to accelerate atmospheric escape. In this work, we present results on laboratory investigations of UV radiation effects simulating two different stellar flare energies on laboratory-produced exoplanet hazes made under conditions analogous to water-world atmospheres. We find that both simulated flares altered the overall transmittance and reflectance of the hazes, and higher energy "flares" make those alterations more pronounced. On a larger scale, these laboratory-made hazes show potential signs of degradation over the simulated flaring period. Our results provide insight into the effects that stellar flaring events have on potential exoplanet haze composition and the ability for water-world-like exoplanets to retain their atmospheres.

astro-ph.EP↗

arXiv2025

Optical Skyrmions in Waveguides

Optical skyrmions are topologically non-trivial polarization fields which have recently attracted attention due to their potential use in high density data applications such as optical communications, photonic computing and more. An important hurdle in utilizing optical skyrmions for such applications is establishing conditions under which their topological structure remains preserved during propagation: while results of this type already exist for paraxial beams in free-space propagation, the critical case relevant to modern applications involves propagation in confined media, such as waveguide systems. In this paper, we demonstrate for the first time that, within a conducting waveguide, the preservation of the skyrmion number during propagation is determined by the presence of so-called topologically stabilizing modes. If such a mode is present, not only will topological protection hold despite the transverse polarization profile changing due to modal dispersion, but there is also a degree of robustness to variations in the coefficients of TE and TM modes present. Lastly, we demonstrate how a generalized skyrmion number can recover topological protection in situations where the usual skyrmion number is not preserved. Our methods open new avenues for robust high-dimensional information manipulation in waveguiding systems.

physics.optics↗

arXiv2025

Intensity adaptive optics

Adaptive optics (AO) is a powerful tool employed across various research fields, from aerospace to microscopy. Traditionally, AO has focused on correcting optical phase aberrations, with recent advances extending to polarisation compensation. However, intensity errors are also prevalent in optical systems, yet effective correction methods are still in their infancy. Here, we introduce a novel AO approach, termed intensity adaptive optics (I-AO), which employs a dual-feedback loop mechanism to first address non-uniform intensity distribution and subsequently compensate for energy loss at the pupil plane. We demonstrate that I-AO can operate in both sensor-based and sensorless formats and validate its feasibility by quantitatively analysing the focus quality of an aberrated system. This technique expands the AO toolkit, paving the way for next-generation AO technology.

physics.optics↗

arXiv2025

Diffusion-based Virtual Staining from Polarimetric Mueller Matrix Imaging

Polarization, as a new optical imaging tool, has been explored to assist in the diagnosis of pathology. Moreover, converting the polarimetric Mueller Matrix (MM) to standardized stained images becomes a promising approach to help pathologists interpret the results. However, existing methods for polarization-based virtual staining are still in the early stage, and the diffusion-based model, which has shown great potential in enhancing the fidelity of the generated images, has not been studied yet. In this paper, a Regulated Bridge Diffusion Model (RBDM) for polarization-based virtual staining is proposed. RBDM utilizes the bidirectional bridge diffusion process to learn the mapping from polarization images to other modalities such as H\&E and fluorescence. And to demonstrate the effectiveness of our model, we conduct the experiment on our manually collected dataset, which consists of 18,000 paired polarization, fluorescence and H\&E images, due to the unavailability of the public dataset. The experiment results show that our model greatly outperforms other benchmark methods. Our dataset and code will be released upon acceptance.

eess.IV↗

arXiv2025

Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?

The emergence of foundation models in computational pathology has transformed histopathological image analysis, with whole slide imaging (WSI) diagnosis being a core application. Traditionally, weakly supervised fine-tuning via multiple instance learning (MIL) has been the primary method for adapting foundation models to WSIs. However, in this work we present a key experimental finding: a simple nonlinear mapping strategy combining mean pooling and a multilayer perceptron, called SiMLP, can effectively adapt patch-level foundation models to slide-level tasks without complex MIL-based learning. Through extensive experiments across diverse downstream tasks, we demonstrate the superior performance of SiMLP with state-of-the-art methods. For instance, on a large-scale pan-cancer classification task, SiMLP surpasses popular MIL-based methods by 3.52%. Furthermore, SiMLP shows strong learning ability in few-shot classification and remaining highly competitive with slide-level foundation models pretrained on tens of thousands of slides. Finally, SiMLP exhibits remarkable robustness and transferability in lung cancer subtyping. Overall, our findings challenge the conventional MIL-based fine-tuning paradigm, demonstrating that a task-agnostic representation strategy alone can effectively adapt foundation models to WSI analysis. These insights offer a unique and meaningful perspective for future research in digital pathology, paving the way for more efficient and broadly applicable methodologies.

cs.CV↗

arXiv2025

Textoon: Generating Vivid 2D Cartoon Characters from Text Descriptions

The 2D cartoon style is a prominent art form in digital character creation, particularly popular among younger audiences. While advancements in digital human technology have spurred extensive research into photorealistic digital humans and 3D characters, interactive 2D cartoon characters have received comparatively less attention. Unlike 3D counterparts, which require sophisticated construction and resource-intensive rendering, Live2D, a widely-used format for 2D cartoon characters, offers a more efficient alternative, which allows to animate 2D characters in a manner that simulates 3D movement without the necessity of building a complete 3D model. Furthermore, Live2D employs lightweight HTML5 (H5) rendering, improving both accessibility and efficiency. In this technical report, we introduce Textoon, an innovative method for generating diverse 2D cartoon characters in the Live2D format based on text descriptions. The Textoon leverages cutting-edge language and vision models to comprehend textual intentions and generate 2D appearance, capable of creating a wide variety of stunning and interactive 2D characters within one minute. The project homepage is https://human3daigc.github.io/Textoon_webpage/.

cs.CV↗

arXiv2024

Unlocking adaptive digital pathology through dynamic feature learning

Foundation models have revolutionized the paradigm of digital pathology, as they leverage general-purpose features to emulate real-world pathological practices, enabling the quantitative analysis of critical histological patterns and the dissection of cancer-specific signals. However, these static general features constrain the flexibility and pathological relevance in the ever-evolving needs of clinical applications, hindering the broad use of the current models. Here we introduce PathFiT, a dynamic feature learning method that can be effortlessly plugged into various pathology foundation models to unlock their adaptability. Meanwhile, PathFiT performs seamless implementation across diverse pathology applications regardless of downstream specificity. To validate PathFiT, we construct a digital pathology benchmark with over 20 terabytes of Internet and real-world data comprising 28 H\&E-stained tasks and 7 specialized imaging tasks including Masson's Trichrome staining and immunofluorescence images. By applying PathFiT to the representative pathology foundation models, we demonstrate state-of-the-art performance on 34 out of 35 tasks, with significant improvements on 23 tasks and outperforming by 10.20% on specialized imaging tasks. The superior performance and versatility of PathFiT open up new avenues in computational pathology.

eess.IV↗

arXiv2024

Generalized Skyrmions

Skyrmions are important topologically non-trivial fields characteristic of models spanning scales from the microscopic to the cosmological. However, the Skyrmion number can only be defined for fields with specific boundary conditions, limiting its use in broader contexts. Here, we address this issue through a generalized notion of the Skyrmion derived from the De Rham cohomology of compactly supported forms. This allows for the definition of an entirely new $\coprod_{i=1}^\infty \mathbb{Z}^i$-valued topological number that assigns a tuple of integers $(a_1, \ldots, a_k)\in \mathbb{Z}^k$ to a field instead of a single number, with no restrictions to its boundary. The notion of the generalized Skyrmion presented in this paper is completely abstract and can be applied to vector fields in any discipline, not unlike index theory within dynamical systems. To demonstrate the power of our new formalism, we focus on the propagation of optical polarization fields and show that our newly defined generalized Skyrmion number significantly increases the dimension of data that can be stored within the field while also demonstrating strong robustness. Our work represents a fundamental paradigm shift away from the study of fields with natural topological character to engineered fields that can be artificially embedded with topological structures.

physics.optics↗

arXiv2024

Topological Protection of Optical Skyrmions through Complex Media

Optical Skyrmions have many important properties that make them ideal units for high-density data applications, including the ability to carry digital information through a discrete topological number and the independence of spatially varying polarization to other dimensions. More importantly, the topological nature of the optical Skyrmion heuristically suggests a strong degree of robustness to perturbations, which is crucial for reliably carrying information in noisy environments. However, the study of the topological robustness of optical Skyrmions is still in its infancy. Here, we quantify this robustness precisely by proving that the topological nature of the Skyrmion arises from its structure on the boundary and, by duality, is therefore resilient to complex perturbations provided they respect the relevant boundary conditions of the unperturbed Skyrmion. We then present experimental evidence validating this robustness in the context of paraxial Skyrmion beams against different polarization aberrations. Our work provides a framework for handling various perturbations of Skyrmion fields and offers guarantees of robustness in a general sense. This, in turn, has implications for applications of the optical Skyrmion where their topological nature is exploited explicitly, and, in particular, provides an underpinning for the use of Skyrmions in optical communications and photonic computing.

physics.optics↗

arXiv2024

Low temperature formation of pyridine and (iso)quinoline via neutral neutral reactions

Aromatic molecules represent fundamental building blocks in prebiotic chemistry and are contemplated as vital precursors to DNA and RNA nitrogen bases. However, despite the identification of some 300 molecules in extraterrestrial environments, the pathways to pyridine (C5H5N), pyridinyl (C5H4N), and (iso)quinoline (C9H7N) the simplest representative of mono and bicyclic aromatic molecule carrying nitrogen are elusive. Here, we afford compelling evidence on the gas phase formation of methylene amidogen (H2CN) and cyanomethyl (H2CCN) radicals via molecular beam studies and electronic structure calculations. The modeling of the chemistries of Taurus Molecular Cloud (TMC 1) and Titans atmosphere contemplates a complex chain of reactions synthesizing pyridine, pyridinyl, and (iso)quinoline from H2CN and H2CCN at levels of up to 75%. This study affords unique entry points to precursors of DNA and RNA nitrogen bases in hydrocarbon rich extraterrestrial environments thus changing the way we think about the origin of prebiotic molecules in our Galaxy.

physics.chem-ph↗

arXiv2024

HybridHash: Hybrid Convolutional and Self-Attention Deep Hashing for Image Retrieval

Deep image hashing aims to map input images into simple binary hash codes via deep neural networks and thus enable effective large-scale image retrieval. Recently, hybrid networks that combine convolution and Transformer have achieved superior performance on various computer tasks and have attracted extensive attention from researchers. Nevertheless, the potential benefits of such hybrid networks in image retrieval still need to be verified. To this end, we propose a hybrid convolutional and self-attention deep hashing method known as HybridHash. Specifically, we propose a backbone network with stage-wise architecture in which the block aggregation function is introduced to achieve the effect of local self-attention and reduce the computational complexity. The interaction module has been elaborately designed to promote the communication of information between image blocks and to enhance the visual representations. We have conducted comprehensive experiments on three widely used datasets: CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that the method proposed in this paper has superior performance with respect to state-of-the-art deep hashing methods. Source code is available https://github.com/shuaichaochao/HybridHash.

cs.CV↗