arXiv subjects

Lingling Tao

Publications and source records attributed to Lingling Tao.

9 recordsLinked to original sources

arXiv2026

Universal Design Principles for High-Quality Persistent Spin Textures

Persistent spin texture (PST) describes a unique spin-momentum locking in momentum space that maintains a uniform spin orientation through portions of the Brillouin zone (BZ), enabling exceptionally long spin lifetimes which are essential for applications in spintronics. However, materials exhibiting large BZ regions of high-quality PST, characterized by minimal spin deviation and long spin lifetimes, remain scarce. Here a universal model is introduced to capture the formation of superior PST regions arising from the interplay of spin-orbit fields at different k points. Within this framework, high-quality PSTs are identified in several systems belonging to various point groups. Notably, the nonpolar-chiral compound Na2Sn2O3 exhibits ~0.02 {\AA}-2 high-quality PST region, which can be reversed by the switching of geometric chirality, while AgClO4 (D2d symmetry) exhibits a 0.016 {\AA}-2 PST region. Significantly, Na2Sn2O3 and AgClO4 host persistent spin helices with spin lifetimes of 0.5-7.4 ns and 0.9-2.5 ns, respectively, among the longest reported for PST materials. In addition, both chemical substitutions and the application of pressure are demonstrated as effective routes for engineering high-quality PST. Our findings not only establish a universal principle for high-quality PST, but also provide promising materials across various point groups for the next-generation spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

IAM: Identity-Aware Human Motion and Shape Joint Generation

Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. However, most existing approaches assume identity-neutral motion and generate movements using a canonical body representation, ignoring the strong influence of body morphology on motion dynamics. In practice, attributes such as body proportions, mass distribution, and age significantly affect how actions are performed, and neglecting this coupling often leads to physically inconsistent motions. We propose an identity-aware motion generation framework that explicitly models the relationship between body morphology and motion dynamics. Instead of relying on explicit geometric measurements, identity is represented using multimodal signals, including natural language descriptions and visual cues. We further introduce a joint motion-shape generation paradigm that simultaneously synthesizes motion sequences and body shape parameters, allowing identity cues to directly modulate motion dynamics. Extensive experiments on motion capture datasets and large-scale in-the-wild videos demonstrate improved motion realism and motion-identity consistency while maintaining high motion quality. Project page: https://vjwq.github.io/IAM

cs.CV↗

arXiv2026

LLaMo: Scaling Pretrained Language Models for Unified Motion Understanding and Generation with Continuous Autoregressive Tokens

Recent progress in large models has led to significant advances in unified multimodal generation and understanding. However, the development of models that unify motion-language generation and understanding remains largely underexplored. Existing approaches often fine-tune large language models (LLMs) on paired motion-text data, which can result in catastrophic forgetting of linguistic capabilities due to the limited scale of available text-motion pairs. Furthermore, prior methods typically convert motion into discrete representations via quantization to integrate with language models, introducing substantial jitter artifacts from discrete tokenization. To address these challenges, we propose LLaMo, a unified framework that extends pretrained LLMs through a modality-specific Mixture-of-Transformers (MoT) architecture. This design inherently preserves the language understanding of the base model while enabling scalable multimodal adaptation. We encode human motion into a causal continuous latent space and maintain the next-token prediction paradigm in the decoder-only backbone through a lightweight flow-matching head, allowing for streaming motion generation in real-time (>30 FPS). Leveraging the comprehensive language understanding of pretrained LLMs and large-scale motion-text pretraining, our experiments demonstrate that LLaMo achieves high-fidelity text-to-motion generation and motion-to-text captioning in general settings, especially zero-shot motion generation, marking a significant step towards a general unified motion-language large model.

cs.CV↗

arXiv2024

Intrinsic electromagnetic damping in superconductor-ferromagnet proximity heterostructures

The study of the response of superconducting hybrid structures with magnetic materials to microwave irradiation is necessary for the development of effective superconducting spintronic devices. The role of the magnetic proximity effect (direct and inverse) on the electrical properties of hybrid structures is a pressing issue for its application. We theoretically study the electromagnetic impedance of a thin superconducting (S) film covering a ferromagnetic insulator (FI). An intrinsic damping of microwave irradiation is predicted because of the inverse proximity effect. The system of Usadel equations is solved numerically and self-consistently in the Nambu-Keldysh formalism with boundary conditions for strong spin polarization of the insulator. Based on the calculated Green's function, the features of the bilayer complex conductivity and impedance as a function of the field frequency have been discovered. It is shown how the ferromagnetic proximity effect leads to irremovable damping in the electromagnetic response of such heterostructures. The mechanism related to the formation of triplet Cooper pairs in an S layer at proximity of the FI interface is analyzed. Even gapless superconductivity has been found in a thin S film similar to a magnetic superconductor. The resulting intrinsic damping must be taken into account when designing superconducting devices for microwave applications.

cond-mat.supr-con↗

arXiv2024

General theory for longitudinal nonreciprocal charge transport

The longitudinal nonreciprocal charge transport (NCT) in crystalline materials is a highly non-trivial phenomenon, motivating the design of next generation two-terminal rectification devices (e.g., semiconductor diodes beyond PN junctions). The practical application of such devices is built upon crystalline materials whose longitudinal NCT occurs at room temperature and under low magnetic field. However, materials of this type are rather rare and elusive, and theory guiding the discovery of these materials is lacking. Here, we develop such a theory within the framework of semiclassical Boltzmann transport theory. By symmetry analysis, we classify the complete 122 magnetic point groups with respect to the longitudinal NCT phenomenon. The symmetry-adapted Hamiltonian analysis further uncovers a previously overlooked mechanism for this phenomenon. Our theory guides the first-principles prediction of longitudinal NCT in multiferroic \epsilon-Fe2O3 semiconductor that possibly occurs at room temperature, without the application of external magnetic field. These findings advance our fundamental understandings of longitudinal NCT in crystalline materials, and aid the corresponding materials discoveries.

cond-mat.mtrl-sci↗

arXiv2021

Intrinsic ferroelectricity in Y-doped HfO2 thin films

Ferroelectric HfO2-based materials hold great potential for widespread integration of ferroelectricity into modern electronics due to their robust ferroelectric properties at the nanoscale and compatibility with the existing Si technology. Earlier work indicated that the nanometer crystal grain size was crucial for stabilization of the ferroelectric phase of hafnia. This constraint caused high density of unavoidable structural defects of the HfO2-based ferroelectrics, obscuring the intrinsic ferroelectricity inherited from the crystal space group of bulk HfO2. Here, we demonstrate the intrinsic ferroelectricity in Y-doped HfO2 films of high crystallinity. Contrary to the common expectation, we show that in the 5% Y-doped HfO2 epitaxial thin films, high crystallinity enhances the spontaneous polarization up to a record-high 50 μC/cm2 value at room temperature. The high spontaneous polarization persists at reduced temperature, with polarization values consistent with our theoretical predictions, indicating the dominant contribution from the intrinsic ferroelectricity. The crystal structure of these films reveals the Pca21 orthorhombic phase with a small rhombohedral distortion, underlining the role of the anisotropic stress and strain. These results open a pathway to controlling the intrinsic ferroelectricity in the HfO2-based materials and optimizing their performance in applications.

cond-mat.mtrl-sci↗

arXiv2020

UNOC: Understanding Occlusion for Embodied Presence in Virtual Reality

Tracking body and hand motions in the 3D space is essential for social and self-presence in augmented and virtual environments. Unlike the popular 3D pose estimation setting, the problem is often formulated as inside-out tracking based on embodied perception (e.g., egocentric cameras, handheld sensors). In this paper, we propose a new data-driven framework for inside-out body tracking, targeting challenges of omnipresent occlusions in optimization-based methods (e.g., inverse kinematics solvers). We first collect a large-scale motion capture dataset with both body and finger motions using optical markers and inertial sensors. This dataset focuses on social scenarios and captures ground truth poses under self-occlusions and body-hand interactions. We then simulate the occlusion patterns in head-mounted camera views on the captured ground truth using a ray casting algorithm and learn a deep neural network to infer the occluded body parts. In the experiments, we show that our method is able to generate high-fidelity embodied poses by applying the proposed method on the task of real-time inside-out body tracking, finger motion synthesis, and 3-point inverse kinematics.

cs.CV↗

arXiv2020

Effects of Strain and Film Thickness on the Stability of the Rhombohedral Phase of HfO2

The discovery of ferroelectric polarization in HfO2-based ultrathin films has spawned a lot of interest due to their potential applications in data storage. Recently, a new R3m rhombohedral phase was proposed to be responsible for the emergence of ferroelectricity in the [111]-oriented Hf0.5Zr0.5O2 thin films, but the fundamental mechanism of ferroelectric polarization in such films remains poorly understood. In this paper, we employ density-functional-theory calculations to investigate structural and polarization properties of the R3m HfO2 phase. We find that the film thickness and in-plane compressive strain effects play a key role in stabilizing the R3m phase leading to robust ferroelectricity of [111]-oriented R3m HfO2.

cond-mat.mtrl-sci↗

arXiv2019

Disentangling Pose from Appearance in Monochrome Hand Images

Hand pose estimation from the monocular 2D image is challenging due to the variation in lighting, appearance, and background. While some success has been achieved using deep neural networks, they typically require collecting a large dataset that adequately samples all the axes of variation of hand images. It would, therefore, be useful to find a representation of hand pose which is independent of the image appearance~(like hand texture, lighting, background), so that we can synthesize unseen images by mixing pose-appearance combinations. In this paper, we present a novel technique that disentangles the representation of pose from a complementary appearance factor in 2D monochrome images. We supervise this disentanglement process using a network that learns to generate images of hand using specified pose+appearance features. Unlike previous work, we do not require image pairs with a matching pose; instead, we use the pose annotations already available and introduce a novel use of cycle consistency to ensure orthogonality between the factors. Experimental results show that our self-disentanglement scheme successfully decomposes the hand image into the pose and its complementary appearance features of comparable quality as the method using paired data. Additionally, training the model with extra synthesized images with unseen hand-appearance combinations by re-mixing pose and appearance factors from different images can improve the 2D pose estimation performance.

cs.CV↗