arXiv subjects

Xin Tong

Publications and source records attributed to Xin Tong.

At least 91 records · Page 5Linked to original sources

arXiv2025

StructRe: Rewriting for Structured Shape Modeling

Man-made 3D shapes are naturally organized in parts and hierarchies; such structures provide important constraints for shape reconstruction and generation. Modeling shape structures is difficult, because there can be multiple hierarchies for a given shape, causing ambiguity, and across different categories the shape structures are correlated with semantics, limiting generalization. We present StructRe, a structure rewriting system, as a novel approach to structured shape modeling. Given a 3D object represented by points and components, StructRe can rewrite it upward into more concise structures, or downward into more detailed structures; by iterating the rewriting process, hierarchies are obtained. Such a localized rewriting process enables probabilistic modeling of ambiguous structures and robust generalization across object categories. We train StructRe on PartNet data and show its generalization to cross-category and multiple object hierarchies, and test its extension to ShapeNet. We also demonstrate the benefits of probabilistic and generalizable structure modeling for shape reconstruction, generation and editing tasks.

cs.CV↗

arXiv2025

MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision

We present MoGe, a powerful model for recovering 3D geometry from monocular open-domain images. Given a single image, our model directly predicts a 3D point map of the captured scene with an affine-invariant representation, which is agnostic to true global scale and shift. This new representation precludes ambiguous supervision in training and facilitate effective geometry learning. Furthermore, we propose a set of novel global and local geometry supervisions that empower the model to learn high-quality geometry. These include a robust, optimal, and efficient point cloud alignment solver for accurate global shape learning, and a multi-scale local geometry loss promoting precise local geometry supervision. We train our model on a large, mixed dataset and demonstrate its strong generalizability and high accuracy. In our comprehensive evaluation on diverse unseen datasets, our model significantly outperforms state-of-the-art methods across all tasks, including monocular estimation of 3D point map, depth map, and camera field of view. Code and models can be found on our project page.

cs.CV↗

arXiv2025

LPA3D: 3D Room-Level Scene Generation from In-the-Wild Images

Generating realistic, room-level indoor scenes with semantically plausible and detailed appearances from in-the-wild images is crucial for various applications in VR, AR, and robotics. The success of NeRF-based generative methods indicates a promising direction to address this challenge. However, unlike their success at the object level, existing scene-level generative methods require additional information, such as multiple views, depth images, or semantic guidance, rather than relying solely on RGB images. This is because NeRF-based methods necessitate prior knowledge of camera poses, which is challenging to approximate for indoor scenes due to the complexity of defining alignment and the difficulty of globally estimating poses from a single image, given the unseen parts behind the camera. To address this challenge, we redefine global poses within the framework of Local-Pose-Alignment (LPA) -- an anchor-based multi-local-coordinate system that uses a selected number of anchors as the roots of these coordinates. Building on this foundation, we introduce LPA-GAN, a novel NeRF-based generative approach that incorporates specific modifications to estimate the priors of camera poses under LPA. It also co-optimizes the pose predictor and scene generation processes. Our ablation study and comparisons with straightforward extensions of NeRF-based object generative methods demonstrate the effectiveness of our approach. Furthermore, visual comparisons with other techniques reveal that our method achieves superior view-to-view consistency and semantic normality.

cs.CV↗

arXiv2025

FeatInsight: An Online ML Feature Management System on 4Paradigm Sage-Studio Platform

Feature management is essential for many online machine learning applications and can often become the performance bottleneck (e.g., taking up to 70% of the overall latency in sales prediction service). Improper feature configurations (e.g., introducing too many irrelevant features) can severely undermine the model's generalization capabilities. However, managing online ML features is challenging due to (1) large-scale, complex raw data (e.g., the 2018 PHM dataset contains 17 tables and dozens to hundreds of columns), (2) the need for high-performance, consistent computation of interdependent features with complex patterns, and (3) the requirement for rapid updates and deployments to accommodate real-time data changes. In this demo, we present FeatInsight, a system that supports the entire feature lifecycle, including feature design, storage, visualization, computation, verification, and lineage management. FeatInsight (with OpenMLDB as the execution engine) has been deployed in over 100 real-world scenarios on 4Paradigm's Sage Studio platform, handling up to a trillion-dimensional feature space and enabling millisecond-level feature updates. We demonstrate how FeatInsight enhances feature design efficiency (e.g., for online product recommendation) and improve feature computation performance (e.g., for online fraud detection). The code is available at https://github.com/4paradigm/FeatInsight.

cs.DB↗

arXiv2025

Enhanced anomalous Hall effect in the topological Kagome metal Cs(V$_{1-x}$Mn$_x$)$_3$Sb$_5$

As a fundamental physical phenomenon, achieving and controlling a large anomalous Hall effect (AHE) is crucial for advancing the understanding of topological physics and for developing applied technologies in spintronics. The recently discovered topological Kagome metal $A$V$_3$Sb$_5$ ($A =$ K, Rb, Cs)exhibits a significant AHE along with charge density wave (CDW) and superconductivity, providing an ideal platform to study the interactions between nontrivial band topology, CDW, and superconductivity. In this study, we systematically investigated the evolution of CDW, superconductivity, and AHE in electron (Mn)-doped Cs(V$_{1-x}$Mn$_x$)$_3$Sb$_5$ single crystals. The experimental results show that electron doping rapidly suppresses superconductivity, while the CDW order remains relatively robust. Meanwhile, a significantly enhanced AHE, with a maximum anomalous Hall conductivity (AHC) of ~25331 \Ohm ^{-1}\cm^{-1} and an anomalous Hall angle of 6.66% occurs at a relatively low doping level of $x = 0.03$. Based on the Tian-Ye-Jin (TYJ) scaling model, such a significant enhancement AHC is mainly dominated by the skew scattering. We speculated enhanced skew scattering between electrons and Mn originating from the strengthened spin-orbital coupling. Our finding provides important guidance for the design and development of transverse transport properties in topological Kagome materials.

cond-mat.str-el↗

arXiv2025

Optimal Riemannian metric for Poincaré inequalities and how to ideally precondition Langevin dynamics

Poincaré inequality is a fundamental property that rises naturally in different branches of mathematics. The associated Poincaré constant plays a central role in many applications since it governs the convergence of various practical algorithms. For instance, the convergence rate of the Langevin dynamics is exactly given by the Poincaré constant. This paper investigates a Riemannian version of Poincaré inequality where a positive definite weighting matrix field (\emph{i.e.} a Riemannian metric) is introduced to improve the Poincaré constant, and therefore the performances of the associated algorithm. Assuming the underlying measure is a \emph{moment measure}, we show that an optimal metric exists and the resulting Poincaré constant is 1. We demonstrate that such optimal metric is necessarily a \emph{Stein kernel}, offering a novel perspective on these complex but central mathematical objects that are hard to obtain in practice. We further discuss how to numerically obtain the optimal metric by deriving an implementable optimization algorithm. The resulting method is illustrated in a few simple but nontrivial examples, where solutions are revealed to be rather sophisticated. We also demonstrate how to design efficient Langevin-based sampling schemes by utilizing the precomputed optimal metric as a preconditioner.

math.PR↗

arXiv2025

Towards Typologically Aware Rescoring to Mitigate Unfaithfulness in Lower-Resource Languages

Multilingual large language models (LLMs) are known to more frequently generate non-faithful output in resource-constrained languages (Guerreiro et al., 2023 - arXiv:2303.16104), potentially because these typologically diverse languages are underrepresented in their training data. To mitigate unfaithfulness in such settings, we propose using computationally light auxiliary models to rescore the outputs of larger architectures. As proof of the feasibility of such an approach, we show that monolingual 4-layer BERT models pretrained from scratch on less than 700 MB of data without fine-tuning are able to identify faithful summaries with a mean accuracy of 88.33% in three genetically unrelated languages that differ in their morphological complexity - Vietnamese, Polish and Georgian. The same hyperparameter combination moreover generalises well to three other tasks, suggesting applications for rescoring beyond improving faithfulness. In order to inform typologically aware model selection, we also investigate how morphological complexity interacts with regularisation, model depth and training objectives, ultimately demonstrating that morphologically complex languages are more likely to benefit from dropout, while across languages downstream performance is enhanced most by shallow architectures as well as training using the standard BERT objectives.

cs.CL↗

arXiv2025

Enhanced deep-freezing magneto- and elasto-caloric effects by modifying lattice anharmonicity and electronic structures

Designing the high performance magneto or elastocaloric effect in NiMnIn alloys with spin-lattice coupling in a deep freezing temperature range of 200 K to 255 K is challenging due to the limited lattice entropy change and large negative contribution of magnetic entropy change during phase transitions. In this work, we systematically study the first order magneto-structural transition in NiMnIn based alloys by in-situ microstructural characterizations, physical property measurements, and first principles calculations. A multi element alloying strategy involving Cu and Ga co doping is proposed to manipulate the phase transition. The co doping reduces the lattice anharmonicity and thermal expansion coefficient of the martensitic phase, leading to an increase in the unit cell volume change and lattice entropy change. It also modifies the electronic density of states, causing a decrease in the magnetization change .The relief of the lattice mismatch reduces hysteresis losses in the refrigeration cycle. These synergetic effects yield excellent magneto and elastocaloric effects,with the effective magnetocaloric refrigeration capacity reaching up to 182 J/kg under the magnetic field of 5 T or an adiabatic temperature change of -4 K under a low field of 1.5 T and the elastocaloric coefficient of performance to 30 or an adiabatic temperature change of -7 K with the strain of 5% at 230 K, offering a potential solution for solid-state deep-freezing refrigeration.

cond-mat.mtrl-sci↗

arXiv2025

Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking

Classical Transformer-based line segment detection methods have delivered impressive results. However, we observe that some accurately detected line segments are assigned low confidence scores during prediction, causing them to be ranked lower and potentially suppressed. Additionally, these models often require prolonged training periods to achieve strong performance, largely due to the necessity of bipartite matching. In this paper, we introduce RANK-LETR, a novel Transformer-based line segment detection method. Our approach leverages learnable geometric information to refine the ranking of predicted line segments by enhancing the confidence scores of high-quality predictions in a posterior verification step. We also propose a new line segment proposal method, wherein the feature point nearest to the centroid of the line segment directly predicts the location, significantly improving training efficiency and stability. Moreover, we introduce a line segment ranking loss to stabilize rankings during training, thereby enhancing the generalization capability of the model. Experimental results demonstrate that our method outperforms other Transformer-based and CNN-based approaches in prediction accuracy while requiring fewer training epochs than previous Transformer-based models.

cs.CV↗

arXiv2025

Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems

We develop a conformal inference method to construct a joint confidence region for a given group of missing entries within a sparsely observed matrix, focusing primarily on entries from the same column. Our method is model-agnostic and can be combined with any ``black-box'' matrix completion algorithm to provide reliable uncertainty estimation for group-level recommendations. For example, in the context of movie recommendations, it is useful to quantify the uncertainty in the ratings assigned by all members of a group to the same movie, enabling more informed decision-making when individual preferences may conflict. Unlike existing conformal techniques, which estimate uncertainty for one individual at a time, our method provides stronger group-level guarantees by assembling a structured calibration dataset that mimics the dependencies expected in the test group. To achieve this, we introduce a generalized weighted conformalization framework that addresses the lack of exchangeability arising from structured calibration, introducing several innovations to overcome associated computational challenges. We demonstrate the practicality and effectiveness of our approach through extensive numerical experiments and an analysis of the MovieLens 100K dataset.

stat.ME↗

arXiv2025

Designing LLM-simulated Immersive Spaces to Enhance Autistic Children's Social Affordances Understanding

One of the key challenges faced by autistic children is understanding social affordances in complex environments, which further impacts their ability to respond appropriately to social signals. In traffic scenarios, this impairment can even lead to safety concerns. In this paper, we introduce an LLM-simulated immersive projection environment designed to improve this ability in autistic children while ensuring their safety. We first propose 17 design considerations across four major categories, derived from a comprehensive review of previous research. Next, we developed a system called AIroad, which leverages LLMs to simulate drivers with varying social intents, expressed through explicit multimodal social signals. AIroad helps autistic children bridge the gap in recognizing the intentions behind behaviors and learning appropriate responses through various stimuli. A user study involving 14 participants demonstrated that this technology effectively engages autistic children and leads to significant improvements in their comprehension of social affordances in traffic scenarios. Additionally, parents reported high perceived usability of the system. These findings highlight the potential of combining LLM technology with immersive environments for the functional rehabilitation of autistic children in the future.

cs.HC↗

arXiv2025

SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Instance Segmentation

We introduce SAMPro3D for zero-shot instance segmentation of 3D scenes. Given the 3D point cloud and multiple posed RGB-D frames of 3D scenes, our approach segments 3D instances by applying the pretrained Segment Anything Model (SAM) to 2D frames. Our key idea involves locating SAM prompts in 3D to align their projected pixel prompts across frames, ensuring the view consistency of SAM-predicted masks. Moreover, we suggest selecting prompts from the initial set guided by the information of SAM-predicted masks across all views, which enhances the overall performance. We further propose to consolidate different prompts if they are segmenting different surface parts of the same 3D instance, bringing a more comprehensive segmentation. Notably, our method does not require any additional training. Extensive experiments on diverse benchmarks show that our method achieves comparable or better performance compared to previous zero-shot or fully supervised approaches, and in many cases surpasses human annotations. Furthermore, since our fine-grained predictions often lack annotations in available datasets, we present ScanNet200-Fine50 test data which provides fine-grained annotations on 50 scenes from ScanNet200 dataset. The project page can be accessed at https://mutianxu.github.io/sampro3d/.

cs.CV↗

arXiv2025

Polarization-Analyzed Small-Angle Neutron Scattering with an $\textit{in-situ}$ $^{3}$He neutron spin filter at the China Spallation Neutron Source

Polarization-analyzed small-angle neutron scattering (PASANS) is an advanced technique that enables the selective investigation of magnetic scattering phenomena in magnetic materials and distinguishes coherent scattering obscured by incoherent backgrounds, making it particularly valuable for cutting-edge research. The successful implementation of PASANS in China was achieved for the first time at the newly commissioned Very Small Angle Neutron Scattering (VSANS) instrument at the China Spallation Neutron Source (CSNS). This technique employs a combination of a double-V cavity supermirror polarizer and a radio frequency (RF) neutron spin flipper to manipulate the polarization of the incident neutrons. The scattered neutron polarization is stably analyzed by a specially designed $\textit{in-situ}$ optical pumping $^{3}$He neutron spin filter, which covers a spatially symmetric scattering angle coverage of about 4.8 $^{\circ}$. A comprehensive PASANS data reduction method, aimed at pulsed neutron beams, has been established and validated with a silver behenate powder sample, indicating a maximum momentum transfer coverage of approximately 0.25 Å $^{-1}$.

physics.ins-det↗

arXiv2024

Spin-Weighted Spherical Harmonics for Polarized Light Transport

The objective of polarization rendering is to simulate the interaction of light with materials exhibiting polarization-dependent behavior. However, integrating polarization into rendering is challenging and increases computational costs significantly. The primary difficulty lies in efficiently modeling and computing the complex reflection phenomena associated with polarized light. Specifically, frequency-domain analysis, essential for efficient environment lighting and storage of complex light interactions, is lacking. To efficiently simulate and reproduce polarized light interactions using frequency-domain techniques, we address the challenge of maintaining continuity in polarized light transport represented by Stokes vectors within angular domains. The conventional spherical harmonics method cannot effectively handle continuity and rotation invariance for Stokes vectors. To overcome this, we develop a new method called polarized spherical harmonics (PSH) based on the spin-weighted spherical harmonics theory. Our method provides a rotation-invariant representation of Stokes vector fields. Furthermore, we introduce frequency domain formulations of polarized rendering equations and spherical convolution based on PSH. We first define spherical convolution on Stokes vector fields in the angular domain, and it also provides efficient computation of polarized light transport, nearly on an entry-wise product in the frequency domain. Our frequency domain formulation, including spherical convolution, led to the development of the first real-time polarization rendering technique under polarized environmental illumination, named precomputed polarized radiance transfer, using our polarized spherical harmonics. Results demonstrate that our method can effectively and accurately simulate and reproduce polarized light interactions in complex reflection phenomena.

cs.GR↗

arXiv2024

$\infty$-Categorical Generalized Langlands Correspondence II: Langlands Program Formalism

We extend the Langlands program in various subprograms with certain different generalizations: (1) Mixed-parity functorial perturbation of the usual Langlands program after Fargues-Scholze in all characteristics; (2) Robba-Frobenius sheafified functorial perturbation of the usual Langlands program after Fargues-Scholze and Kedlaya-Liu in all characteristics; (3) Arithmetic $D$-module theoretic functorial perturbation of the usual Langlands program after Fargues-Scholze and Abe-Kedlaya-Xu. In certain localized setting we construct Langlands correspondence from smooth representation side to Weil side through geometrized generalized Bernstein center.

math.RT↗

arXiv2024

VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time

We introduce VASA, a framework for generating lifelike talking faces with appealing visual affective skills (VAS) given a single static image and a speech audio clip. Our premiere model, VASA-1, is capable of not only generating lip movements that are exquisitely synchronized with the audio, but also producing a large spectrum of facial nuances and natural head motions that contribute to the perception of authenticity and liveliness. The core innovations include a holistic facial dynamics and head movement generation model that works in a face latent space, and the development of such an expressive and disentangled face latent space using videos. Through extensive experiments including evaluation on a set of new metrics, we show that our method significantly outperforms previous methods along various dimensions comprehensively. Our method not only delivers high video quality with realistic facial and head dynamics but also supports the online generation of 512x512 videos at up to 40 FPS with negligible starting latency. It paves the way for real-time engagements with lifelike avatars that emulate human conversational behaviors.

cs.CV↗

arXiv2024

Bose-Einstein condensation of a two-magnon bound state in a spin-one triangular lattice

In ordered magnets, the elementary excitations are spin waves (magnons), which obey Bose-Einstein statistics. Similarly to Cooper pairs in superconductors, magnons can be paired into bound states under attractive interactions. The Zeeman coupling to a magnetic field is able to tune the particle density through a quantum critical point (QCP), beyond which a "hidden order" is predicted to exist. Here we report direct observation of the Bose-Einstein condensation (BEC) of the two-magnon bound state in Na$_2$BaNi(PO$_4$)$_2$. Comprehensive thermodynamic measurements confirmed the two-dimensional BEC-QCP at the saturation field. Inelastic neutron scattering experiments were performed to establish the microscopic model. An exact solution revealed stable 2-magnon bound states that were further confirmed by electron spin resonance and nuclear magnetic resonance experiments, demonstrating that the QCP is due to the pair condensation and the phase below saturation field is likely the long-sought-after spin nematic phase.

cond-mat.str-el↗

arXiv2024

Breaking the Midas Spell:Understanding Progressive Novice-AI Collaboration in Spatial Design

In spatial design, Artificial Intelligence (AI) tools often generate the entire spatial design outcome in a single automated step, rather than engaging users in a deepening and iterative process. This significantly reduces users' involvement, learning, and creative capabilities, leading to a superficial understanding of spatial design. We conducted a Wizard-of-Oz study, where Novices and AI (acted by experimenters) worked together to finish spatial design tasks using various AI models. We identified typical function and workflow patterns adopted by the participants, leading to the understanding of the opportunities and challenges in the human-AI co-creation process. Based on insights gathered from this research, we proposed some design implications of the novice-AI collaboration system that aims to democratize spatial design through a progressive, iterative co-creation process.

cs.HC↗