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Weiwei Xu

Publications and source records attributed to Weiwei Xu.

At least 19 recordsLinked to original sources
arXiv2026

Robust Global Structure-from-Motion via View Graph Pruning

Structure-from-Motion (SfM) aims to estimate camera poses and reconstruct 3D structures from a collection of unordered images. Compared with incremental SfM, global SfM achieves better scalability by jointly estimating camera poses based on a view graph constructed from pairwise correspondences. However, its performance is highly sensitive to erroneous edges caused by visually ambiguous matches, which may lead to incorrect camera registration and reconstruction artifacts. In this work, we propose a subgraph-guided view graph pruning framework for robust global SfM. Our key idea is to exploit the internal consistency of reliable subgraphs to identify and remove unreliable connections. Specifically, we first partition the view graph into locally consistent subgraphs and perform global SfM within each subgraph to obtain reliable camera poses. We then apply RANSAC-based edge pruning across subgraphs to remove inconsistent edges, and finally perform global SfM on the refined view graph. Extensive experiments on ambiguous, sequential, and unordered image datasets demonstrate that our method improves the robustness of global SfM under challenging conditions. Further evaluation with neural rendering shows that the improved camera estimation leads to higher-quality novel view synthesis results.

cs.CV↗
arXiv2026

ERF-GS: Reconstructing Fast Motion from Disjoint Event-RGB Viewpoints

Deep learning-driven representations such as neural radiance fields (NeRFs) and 3D Gaussian splatting (3DGS) have revolutionized the field of dynamic 3D scene reconstruction with improved visual precision and scalability. However, the reconstruction of fast-moving objects remains a challenge; existing methods based on conventional frame-based videos often struggle in scenarios such as sports events and animal videography. We propose an event-RGB fusion Gaussian splatting (ERF-GS) framework that integrates event information into both optimization and densification stages of the Gaussian splatting pipeline, taking advantage of novel event sensors with high frame-rate. Unlike many other event-assisted scene reconstruction methods, ERF-GS was developed using realistic simulation settings and realizes event-based learning detached from RGB inputs. This design enables its application beyond straightforward synthetic data into the realm of natural video with complex layout, low frame rates and severe motion blur. Our experiments show that ERF-GS outperforms both the 4DGS baseline and the concurrent E-D3DGS on different variants of the Neu3D and Nvidia datasets which include blurry RGB frames and disjoint RGB-event viewpoints. Our code is available at https://github.com/andrewbxy/ERF-GS.

cs.CV↗
arXiv2026

HI Depletion Begins Well Beyond the Virial Radius: A FAST Stacking Study of 36 Galaxy Clusters to 5R200

We present a stacking study of the neutral atomic hydrogen (HI) content in and around 36 local galaxy clusters at $z<0.07$, using a combination of the FAST all sky HI survey (FASHI) and the extensive spectroscopic catalog mainly from the Dark Energy Spectroscopic Instrument (DESI). We employ spectral stacking techniques to probe the average HI mass and HI-to-stellar mass ratio ($M_{\rm HI}/M_*$) for member galaxies down to stellar masses of $M_*\sim 10^9M_\odot$, spanning a projected cluster-centric distance to $5R_{200}$. Our analysis reveals a pronounced environmental effect: both $M_{\rm HI}$ and $M_{\rm HI}/M_*$ decrease steadily towards the cluster center, dropping by $\sim 0.5$ dex on average from the outskirts to the core. Crucially, we find that $M_{\rm HI}/M_*$ of galaxies remain lower than the field galaxies even at the $5R_{200}$. This provides direct, statistical evidence for substantial gas stripping and pre-processing in the cluster outskirts, likely occurring in infalling groups and large-scale filaments. By further splitting the sample by $g-r$ color, we show that the HI deficiency persists at fixed galaxy color: even the bluest cluster members exhibit $\sim 0.5$~dex lower $M_{\rm HI}/M_*$ than field galaxies of similar color, reflecting environmental effects on the cold gas reservoir prior to full optical transformation. The total HI mass within clusters and their outskirts agrees broadly with predictions from cosmological simulation. Our results underscore the critical role of the extended cluster environment in quenching galaxies by depleting their cold gas reservoirs well before they enter the dense cluster core.

astro-ph.GA↗
arXiv2026

WildShadowRemover: In-the-Wild Video Shadow Removal via Detail-Preserving Video Diffusion Models

Video shadow removal in the wild remains challenging due to complex illumination, diverse shadow appearances, and limited training data. Despite its importance to numerous vision and graphics applications, it remains largely unexplored in unconstrained real-world scenarios. To address this gap, we present WildShadowRemover, a framework that adapts a pretrained video diffusion model for robust video shadow removal via LoRA fine-tuning. To preserve fine image details while retaining the model's powerful generative prior, we augment the frozen VAE decoder with a detail injection module and introduce a shadow-mask-guided frequency-decomposed modulation module to selectively restore high-frequency textures while suppressing shadow artifacts. Monocular depth priors from Depth Anything 3 further provide geometry-aware guidance under challenging lighting conditions. We also construct WildShadow, a large-scale paired video shadow removal dataset and benchmark, covering diverse synthetic scenes. Extensive experiments demonstrate that our method outperforms existing approaches in shadow removal quality and temporal consistency, producing temporally coherent shadow-free videos with superior visual quality and strong generalization across challenging in-the-wild scenarios.

cs.CV↗
arXiv2026

Curly Hair Simulation using Curly Finite Elements

Realistic simulation of curly hair is challenging due to the tight coupling between macroscopic strand deformation and high-frequency geometric details such as waves and helices. In this paper, we propose a curly hair model that decomposes each strand into curly elements, consisting of a rod-base configuration and an analytically defined high-frequency wrinkles represented by planar waves or volumetric helices, with deformation governed primarily by bending for wavy hair and twisting for spiral hair. A curvature-energy splitting scheme separates stretching, buckling, and bending contributions, and efficient energy approximations improve numerical robustness and reduce computational cost without degrading visual fidelity. We further introduce a hybrid collision handling strategy that combines coarse collision proxies for the base configuration with analytical treatment of high-frequency details, and adapt guide-strand interpolation to our curly finite-element representation to generate dense hair while preserving fine structure. Experiments demonstrate stable, efficient, and visually faithful simulation of curly hair across diverse scenarios.

cs.GR↗
arXiv2026

Decoupling Code Complexity from Newcomer Participation: A Causal Study of AI Coding Agent Adoption in OSS

Open-source projects depend on a steady inflow of newcomers. A growing concern is that AI coding agents (tools such as Cursor and Claude Code that write code from natural-language instructions) will crowd them out, by absorbing the simple tasks that beginners start with and by making code harder to read. We give this concern a causal answer. Using GitHub code search we identify 1,888 projects that adopted an agent, signaled by their first commit of a configuration file. We apply difference-in-differences against matched non-adopting controls, restricting the main analysis to the 603 adopters with a genuine pre-adoption period. We find no evidence of crowding-out: across estimators newcomer inflow shows no significant decline after adoption (point estimates run from a small increase to, under the most conservative trend specification, a slight and insignificant dip), onboarding and retention are unchanged, and a sparse, correlational beginner-task measure (good-first-issue labels, which we cannot test for parallel trends) shows no decline. The feared mechanism is real but decoupled: adoption raises per-function code complexity (about +11% on a cognitive metric for Python, a quarter of the prior estimate, and +3 to 4% in cyclomatic terms across all languages), yet in fixed-unit subsets where complexity rose (Python on the cognitive metric, and all languages on the cyclomatic metric), newcomer participation does not decline. These results suggest that, in established open-source projects, adopting an AI coding agent makes code modestly more complex but does not crowd out the human newcomers that a project depends on: the feared trade-off between AI assistance and human participation does not materialize.

cs.SE↗
arXiv2026

TurboGS: Accelerating 3D Gaussian Splatting via Error-Guided Sparse Pixel Sampling and Optimization

Consumer-level applications require fast optimization of 3D Gaussian Splatting (3DGS) with high-fidelity novel view rendering. However, existing 3DGS acceleration approaches still incur substantial computation on redundant pixels while sacrificing fine details. In this paper, we present TurboGS, an error-guided training framework that accelerates 3DGS by concentrating optimization on perceptually informative pixels. TurboGS is built upon four core components: (1) a tile-wise sparse pixel sampling, which, driven by multi-view reconstruction errors during training, prioritizes challenging regions and skips well-reconstructed ones to avoid redundant gradient computation; (2) a tile-wise structure-aware loss with sparse Normalized Cross-Correlation, which provides sparse yet effective supervision to preserve fine details and stabilize training; (3) an error-driven Gaussian density control strategy, which dynamically allocates model capacity and removes redundant primitives; and (4) a tailored hybrid optimizer that couples Hessian-informed updates with Adam moment damping to stabilize and improve convergence under sparse supervision. Experiments on standard benchmarks demonstrate that TurboGS can deliver on par or superior rendering quality within 100 seconds on a single RTX 5090 GPU card (up to 10x training speedup over vanilla 3DGS).

cs.CV↗
arXiv2026

A governance horizon for ethical-use constraints in open-weight AI models

Ethical constraints on open-weight AI models are both a reflection of societal concerns and a foundation for AI governance policy. They are expected to propagate to downstream derivatives while implemented as voluntary metadata disclosures that must be restated at each generation of reuse. We audit 2,142,823 model repositories on Hugging Face Hub to test whether this disclosure-based governance infrastructure can sustain traceability across deep model lineages. Restriction evidence decays with a half-life of 1.31 derivation steps ($R^2$=0.98), and beyond seven downstream generations at least 80% of descendant models lack sufficient public evidence for a governance determination, a depth boundary we formalize as the governance horizon. Platform-level interventions to restore missing licence metadata reveal that policy design (not enforcement alone) is the binding factor: inheritance-only designs require near-complete enforcement to move the horizon, whereas a mandatory-declaration design that explicitly resolves orphan lineage components shifts the horizon already at moderate enforcement. The structural bottleneck is lineages with no inheritable upstream intent: such orphan components remain undecidable under any inheritance-only policy regardless of enforcement rate, and unresolved upstream nodes additionally create direct downstream undecidability bottlenecks that inheritance rules alone cannot recover. Comparison with PyPI, where governance signals are carried by explicit machine-readable declarations, corroborates that the collapse is topology-specific to open-weight derivation rather than inherent to open ecosystems. These results establish that disclosure-based governance has a shallow, structurally determined reach in open-weight AI, and that achieving deep supply-chain accountability requires provenance mechanisms propagating governance signals through derivation itself.

cs.AI↗
arXiv2026

Distributed Affine Body Dynamics with Adaptive Consensus

Affine Body Dynamics (ABD) within the Incremental Potential Contact (IPC) framework provides accurate simulation of extremely stiff solids exhibiting near-rigid behavior, with strict non-penetration guarantees. However, IPC's globally coupled barrier constraints hinder scalable execution across multiple GPUs and compute nodes. We propose a distributed formulation of ABD using a consensus-based ADMM scheme. Each compute node solves its local ABD subproblem in parallel, followed by a global consensus step that enforces consistency among shared boundary bodies. The proposed method preserves IPC-level robustness and global consistency under distributed execution. Experiments demonstrate stable convergence, non-penetration, and efficient scaling on large-scale scenes across multiple nodes.

cs.GR↗
arXiv2026

Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code

Large Language Models (LLMs) are increasingly changing how code is produced, but generated code still requires human review and validation before it can be adapted or integrated into real-world projects. This makes the readability of LLM-generated code a critical concern. Existing studies have mainly focused on functional correctness and task completion of LLM generated code, leaving open questions about whether it is readable, how its readability fails, and to what extent prompt design can improve it. We therefore investigate the readability of LLM-generated code. We first construct a readability assessment model that integrates textual, structural, program, and visual features. Using this model, we compare human-written code with code generated by representative frontier LLMs across 2,735 scenarios derived from World of Code (WoC) and LeetCode. We further characterize readability issue patterns using thematic analysis and examine prompt design associations through controlled prompt-variant experiments. Our results show that current LLMs produce code that is comparable to human-written code in overall readability. However, this aggregate similarity masks distinct issue patterns, including excessive complexity, redundant comments, and unknown API usage. Prompt analysis further shows that function signature, constraints, and style description are most strongly associated with code readability, although the overall role of prompt design remains bounded. These findings reveal latent readability debt in AI-assisted programming, identify prompt design as a lightweight starting point for improving generated-code readability, and motivate automated support for detecting and mitigating readability issues in future development workflows.

cs.SE↗
arXiv2026

HetScene: Heterogeneity-Aware Diffusion for Dense Indoor Scene Generation

Generating controllable and physically plausible indoor scenes is a pivotal prerequisite for constructing high-fidelity simulation environments for embodied AI. However, existing deeplearning-based methods usually treat all objects as homogeneous instances within a unified generation process. While effective for sparse and simplistic layouts, they struggle to model realistic layouts with dense object arrangements and complex spatial dependencies, leadingto limited scalability and degraded physical plausibility. To deal with these challenges, we revisit indoor layout generation from the perspective of structural heterogeneity and decompose the objects into primary objects and secondary objects according to their distinct roles in shaping a scene. Based on this decomposition, we propose HetScene, a heterogeneous two-stage generation framework that decouples indoor layout synthesis into Structural Layout Generation (SLG) and Contextual Layout Generation (CLG). SLG first generates globally coherent structural layouts with only primary objects conditioned on text descriptions, top-down binary room masks, and spatial relation graphs, establishing a stable global macro-skeleton of large core furniture.

cs.CV↗
arXiv2026

Multiwavelength Analysis of PSR J0437-4715 with Pulse Profile Modeling

We present a multi-wavelength analysis of the nearby millisecond pulsar PSR J0437--4715, combining Hubble Space Telescope (HST) far-ultraviolet, ROSAT soft X-ray, and XMM-Newton X-ray data, to model its broadband emission and energy-resolved pulse profiles, and infer key stellar parameters via Bayesian inference. The broadband emission includes cold thermal, hot thermal, and non-thermal components: cold bulk surface emission is modeled with a non-magnetized partially-ionized hydrogen atmosphere; hot-spot emission adopts the pulse profile modeling technique with a non-magnetized fully-ionized hydrogen atmosphere model; and non-thermal emission is included as a phase-invariant power-law component. By adopting an informative prior on the hot-spot geometry informed by radio polarization position angle measurements, the joint multi-instrument analysis yields a statistically viable and radio-consistent solution with a gravitational mass of 1.38$\pm$0.03~M$_\odot$ and an equatorial circumferential radius of 13.25$_{-0.35}^{+0.34}$~km (68\% confidence intervals). The hot-spot geometry consists of two spherical caps with uniform temperature distributions: the primary hot spot is situated at a colatitude of $\approx$130$^{\circ}$, and the secondary hot spot lies at a colatitude of $\approx$9$^{\circ}$, close to the north pole. It yields tighter radius constraints than HST+ROSAT fits and shifts the radius posterior distribution to larger values relative to NICER-only fits. This work demonstrates the importance of multi-wavelength data in refining neutron star mass-radius measurements and resolving geometric degeneracies.

astro-ph.HE↗
arXiv2026

Continuous Limits of Coupled Flows in Representation Learning

While modern representation learning relies heavily on global error signals, decentralized algorithms driven by local interactions offer a fundamental distributed alternative. However, the macroscopic convergence properties of these discrete dynamics on continuous data manifolds remain theoretically unresolved, notoriously suffering from parameter explosion. We bridge this gap by formalizing decentralized learning as a coupled slow-fast dynamical system on Riemannian manifolds. First, using measure-theoretic limits, we prove that the discrete spatial transitions converge uniformly to an overdamped Langevin stochastic differential equation. Second, via the It\^o-Poisson resolvent and a stochastic extension of LaSalle's Invariance Principle, we establish that the representation weights unconditionally avoid divergence and align strictly with the principal eigenspace of the spatial measure. Finally, we construct a joint Lyapunov functional for the fully coupled spatial-parametric flow. This proves global dissipativity and demonstrates that orthogonally disentangled, linearly separable features emerge spontaneously at the stationary limit. Our framework bridges discrete algorithms with continuous stochastic analysis, providing a formal theoretical baseline for decentralized representation learning.

cs.LG↗
arXiv2026

GAN-based Domain Adaptation for Image-aware Layout Generation in Advertising Poster Design

Layout plays a crucial role in graphic design and poster generation. Recently, the application of deep learning models for layout generation has gained significant attention. This paper focuses on using a GAN-based model conditioned on images to generate advertising poster graphic layouts, requiring a dataset of paired product images and layouts. To address this task, we introduce the Content-aware Graphic Layout Dataset (CGL-Dataset), consisting of 60,548 paired inpainted posters with annotations and 121,000 clean product images. The inpainting artifacts introduce a domain gap between the inpainted posters and clean images. To bridge this gap, we design two GAN-based models. The first model, CGL-GAN, uses Gaussian blur on the inpainted regions to generate layouts. The second model combines unsupervised domain adaptation by introducing a GAN with a pixel-level discriminator (PD), abbreviated as PDA-GAN, to generate image-aware layouts based on the visual texture of input images. The PD is connected to shallow-level feature maps and computes the GAN loss for each input-image pixel. Additionally, we propose three novel content-aware metrics to assess the model's ability to capture the intricate relationships between graphic elements and image content. Quantitative and qualitative evaluations demonstrate that PDA-GAN achieves state-of-the-art performance and generates high-quality image-aware layouts.

cs.LG↗
arXiv2026

Image-aware Layout Generation with User Constraints for Poster Design

Graphic layout is essential in poster generation. Professionals often need to design different layouts for a product image, to ensure they meet specific user requirements. This paper focuses on utilizing a deep-learning model to automatically generate image-aware layouts with user-defined constraints, including layout attributes and partial layouts. Layout attribute constraints require generated layouts to include and exclude elements of specified classes, such as text, logos, underlays, and embellishments. Our model represents different attributes by sampling multidimensional Gaussian noise with different means, and we propose an attribute-consistent loss and an attribute-disentangled loss to ensure that the generated layout satisfies the specified attribute. Partial layout constraints provide our model with incomplete layout information to guide the generation of the remaining elements. We design a partial-constraint loss to incorporate the provided partial layout. Furthermore, we introduce a random mask to diversify the partial layout constraints, which can encourage the model to learn more general latent representations of the provided partial layouts. Both quantitative and qualitative evaluations demonstrate that our model can generate different image-aware layouts according to various user constraints while achieving state-of-the-art performance.

cs.GR↗
arXiv2026

The First Issue Matters: Linking Task-Level Characteristics to Long-Term Newcomer Retention in OSS

Sustaining newcomer participation is critical for the long-term health of open-source communities. Although prior research has explored various task recommendation approaches to help newcomers resolve their first-issue, these methods overlook how characteristics of first-issues may influence newcomers' long-term retention, limiting our understanding of whether initial success leads to sustained participation and hindering effective onboarding design. In this paper, we conduct a large-scale empirical study to examine how first-issue characteristics affect newcomer retention. We combine predictive analysis, interpretability techniques, and causal inference to estimate the causal effects of issue characteristics on retention outcomes. The prediction task supports the interpretation and shows that interaction-related characteristics exhibit stronger associations with retention than intrinsic issue attributes. The causal analysis further reveals that issues reported by moderately experienced contributors, accompanied by moderate discussion intensity and participation from project members, and neutral or slightly negative comment sentiment, have higher retention potential. These findings provide actionable insights for OSS maintainers on designing issue management practices that better support long-term newcomer retention.

cs.SE↗
arXiv2026

A Telescope System for Charge and Position Measurement of High Energy Nuclei

A high-granularity telescope system with a large sensitive area and low material budget has been developed for high-energy heavy ion beam tests. The telescope consists of nine layers of silicon microstrip detectors (SSDs), whose performance was validated through a heavy ion beam test at the CERN SPS. A hybrid machine learning algorithm is proposed to address the challenges of nuclear charge measurement with SSDs. The system achieves a spatial resolution of $\mathcal{O}(1) \,$\SI{}{\micro\metre} and a charge resolution better than 0.16 charge units for nuclei from $Z = 1$ to $Z = 29$, with a sensitive area of $8 \times 8 \, \mathrm{cm}^2$. To the best of our knowledge, this represents the most precise charge and spatial resolution simultaneously achieved by a silicon telescope to date.

physics.ins-det↗
arXiv2026

Beam Test Characterization of Silicon Microstrip Detector Flight-Model Ladders for the AMS-02 Upgrade

The AMS-02 experiment plans to install a new silicon microstrip tracker layer (Layer-0) on top of the existing detector, increasing the cosmic-ray acceptance by a factor of 3. Layer-0 employs a design in which multiple silicon microstrip detectors (SSDs) are connected in series to form long detector ladders. We present a detailed performance study of the flight-model ladders using a 350~GeV mixed hadron beam at the CERN SPS. The study focuses on the following aspects: (i) the performance of ladders with different numbers of SSDs, for which the intrinsic spatial resolution at normal incidence varies from $9.5~\mu\mathrm{m}$ to $11.4~\mu\mathrm{m}$ for ladders composed of 8 to 12 SSDs; (ii) the response consistency for particles impacting on the \emph{Head} and \emph{Tail} regions of the ladder; and (iii) the dependence of the detector performance on the particle incidence angle.

physics.ins-det↗