arXiv subjects

Shilin Hu

Publications and source records attributed to Shilin Hu.

13 recordsLinked to original sources

arXiv2026

Domain-Grounded Candidate Selection for Agentic Image Editing: A Shadow Removal Case

Commercial vision-language models are reshaping computer vision, with visual priors broad enough to rival task-specific systems. This raises a natural question: do they reduce the need for classic, physics-informed low-level vision? We study this through shadow removal, a problem shaped by scene geometry, illumination, materials, and occluders, where paired shadow and shadow-free data are hard to collect at scale. We find that a commercial generative editor, used directly, can produce clean shadow-free edits that preserve surface texture and local appearance. However, this comes with a new failure mode: the same editor can regenerate scene content, hallucinate objects, or misread a shadow as material or geometry, producing plausible but physically wrong edits. We address this with an agentic candidate-selection pipeline: the editor generates a guided probe, an evaluator screens for major failures, retries when needed, samples multiple candidates, filters them, and selects a final result balancing shadow removal against scene preservation. Grounding this process in shadow-formation physics makes it more reliable: prompting the generator and evaluator to treat shadows as illumination effects caused by light occlusion, not material or object structure, measurably improves quality and consistency. On the ShadowRemovalRefine benchmark, our physics-oriented pipeline achieves a CDD of 0.0075, reducing CDD by at least 47% over the strongest prior method. These results suggest that commercial vision-language models do not replace classic low-level vision priors; instead, such priors remain useful for constraining and steering physically underconstrained generation.

cs.CV↗

arXiv2026

Interface Spin-orbit Coupling Induced Room-temperature Ferromagnetic Insulator

Fabricating room-temperature ferromagnetic insulators, which are crucial candidates for next-generation dissipation-free quantum and spintronic devices, remains a significant challenge. In this Letter, we report on the epitaxial synthesis of novel room-temperature ferromagnetic insulating thin films created through the precise construction of (111)-oriented 3d/5d interfaces. Our analysis indicates that, unlike conventional doping methods, the (111)-oriented SrIrO3/La2/3Sr1/3MnO3 (SIO/LSMO) interfaces exhibit markedly enhanced spin-orbit coupling. This enhanced interfacial spin-orbit coupling strengthens the electron-phonon coupling in LSMO, thereby shortening the electronic mean free path. As a result, the intrinsic metallicity of LSMO is suppressed, giving rise to a new ferromagnetic insulating phase that emerges between the ferromagnetic metal and paramagnetic insulator regimes of the LSMO phase diagram. Furthermore, the temperature window of the ferromagnetic insulating phase can be tuned by precisely controlling the thickness of the LSMO layers. Our Letter reveals a new strategy for developing ferromagnetic insulators by engineering 3d/5d interfaces and orientations, paving a way for the development of novel dissipation-free quantum and spintronic devices.

cond-mat.str-el↗

arXiv2025

Embedding Physical Reasoning into Diffusion-Based Shadow Generation

Generating realistic shadows for inserted objects requires reasoning about scene geometry and illumination. However, most existing methods operate purely in image space, leaving the physical relationship between objects, lighting, and shadows to be learned implicitly, often resulting in misaligned or implausible shadows. We instead ground shadow generation in the physics of shadow formation. Given a composite image and an object mask, we recover approximate scene geometry and estimate a dominant light direction to derive a physics-grounded shadow estimate via geometric reasoning. While coarse, this estimate provides a spatial anchor for shadow placement. Because illumination cannot always be uniquely inferred from a single image, we predict confidence scores for both lighting and shadow cues and use them to regulate their influence during generation. These cues, shadow mask, light direction, and their confidences, condition a diffusion-based generator that refines the estimate into a realistic shadow. Experiments on DESOBAV2 show that our method improves both shadow realism and localization, achieving 23% lower shadow-region RMSE and 30% lower shadow-region BER over prior state-of-the-art.

cs.CV↗

arXiv2025

Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning

Shadows encode rich information about scene geometry and illumination, yet existing methods either predict a unified shadow mask or overlook attached shadows entirely. We address this gap by proposing a framework for jointly detecting cast and attached shadows through explicit physical modeling of light direction and surface geometry under a dominant directional-light setting. Our approach is grounded in a simple observation: surfaces facing away from the light source tend to fall into shadow. We exploit the reciprocal relationship between shadow formation and light estimation to construct a closed feedback loop, a dual-module architecture in which a shadow detection module and a light estimation module iteratively refine each other. At each pass, updated light estimates, together with surface normals, produce partial attached shadow maps that guide detection, while improved shadow predictions sharpen light estimation. To support training and evaluation, we introduce a dataset of 1,458 images with manually annotated cast and attached shadow masks sourced from three existing benchmarks. Experiments demonstrate that our proposed method outperforms prior methods, with at least a 33% reduction in attached-shadow BER, while maintaining strong full-shadow and cast-shadow performance.

cs.CV↗

arXiv2024

MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields

Current methods for extracting intrinsic image components, such as reflectance and shading, primarily rely on statistical priors. These methods focus mainly on simple synthetic scenes and isolated objects and struggle to perform well on challenging real-world data. To address this issue, we propose MLI-NeRF, which integrates \textbf{M}ultiple \textbf{L}ight information in \textbf{I}ntrinsic-aware \textbf{Ne}ural \textbf{R}adiance \textbf{F}ields. By leveraging scene information provided by different light source positions complementing the multi-view information, we generate pseudo-label images for reflectance and shading to guide intrinsic image decomposition without the need for ground truth data. Our method introduces straightforward supervision for intrinsic component separation and ensures robustness across diverse scene types. We validate our approach on both synthetic and real-world datasets, outperforming existing state-of-the-art methods. Additionally, we demonstrate its applicability to various image editing tasks. The code and data are publicly available.

cs.CV↗

arXiv2024

Shadow Removal Refinement via Material-Consistent Shadow Edges

Shadow boundaries can be confused with material boundaries as both exhibit sharp changes in luminance or contrast within a scene. However, shadows do not modify the intrinsic color or texture of surfaces. Therefore, on both sides of shadow edges traversing regions with the same material, the original color and textures should be the same if the shadow is removed properly. These shadow/shadow-free pairs are very useful but hard-to-collect supervision signals. The crucial contribution of this paper is to learn how to identify those shadow edges that traverse material-consistent regions and how to use them as self-supervision for shadow removal refinement during test time. To achieve this, we fine-tune SAM, an image segmentation foundation model, to produce a shadow-invariant segmentation and then extract material-consistent shadow edges by comparing the SAM segmentation with the shadow mask. Utilizing these shadow edges, we introduce color and texture-consistency losses to enhance the shadow removal process. We demonstrate the effectiveness of our method in improving shadow removal results on more challenging, in-the-wild images, outperforming the state-of-the-art shadow removal methods. Additionally, we propose a new metric and an annotated dataset for evaluating the performance of shadow removal methods without the need for paired shadow/shadow-free data.

cs.CV↗

arXiv2024

Learning Relighting and Intrinsic Decomposition in Neural Radiance Fields

The task of extracting intrinsic components, such as reflectance and shading, from neural radiance fields is of growing interest. However, current methods largely focus on synthetic scenes and isolated objects, overlooking the complexities of real scenes with backgrounds. To address this gap, our research introduces a method that combines relighting with intrinsic decomposition. By leveraging light variations in scenes to generate pseudo labels, our method provides guidance for intrinsic decomposition without requiring ground truth data. Our method, grounded in physical constraints, ensures robustness across diverse scene types and reduces the reliance on pre-trained models or hand-crafted priors. We validate our method on both synthetic and real-world datasets, achieving convincing results. Furthermore, the applicability of our method to image editing tasks demonstrates promising outcomes.

cs.CV↗

arXiv2021

Temporal Feature Warping for Video Shadow Detection

While single image shadow detection has been improving rapidly in recent years, video shadow detection remains a challenging task due to data scarcity and the difficulty in modelling temporal consistency. The current video shadow detection method achieves this goal via co-attention, which mostly exploits information that is temporally coherent but is not robust in detecting moving shadows and small shadow regions. In this paper, we propose a simple but powerful method to better aggregate information temporally. We use an optical flow based warping module to align and then combine features between frames. We apply this warping module across multiple deep-network layers to retrieve information from neighboring frames including both local details and high-level semantic information. We train and test our framework on the ViSha dataset. Experimental results show that our model outperforms the state-of-the-art video shadow detection method by 28%, reducing BER from 16.7 to 12.0.

cs.CV↗

arXiv2019

Quantum dynamics of atomic Rydberg excitation in strong laser fields

Neutral atoms have been observed to survive intense laser pulses in high Rydberg states with surprisingly large probability. Only with this Rydberg-state excitation (RSE) included is the picture of intense-laser-atom interaction complete. Various mechanisms have been proposed to explain the underlying physics. However, neither one can explain all the features observed in experiments and in time-dependent Schrödinger equation (TDSE) simulations. Here we propose a fully quantum-mechanical model based on the strong-field approximation (SFA). It well reproduces the intensity dependence of RSE obtained by the TDSE, which exhibits a series of modulated peaks. They are due to recapture of the liberated electron and the fact that the pertinent probability strongly depends on the position and the parity of the Rydberg state. We also present measurements of RSE in xenon at 800 nm, which display the peak structure consistent with the calculations.

physics.atom-ph↗

arXiv2019

Accuracy of the semiclassical picture of photoionization in intense laser fields

In the semiclassical picture of photoionization process in intense laser fields, the ionization rate solely depends on the amplitude of the electric field and the final photoelectron momentum corresponds to the instant of ionization of the photoelectron, however, this picture has never been checked rigorously. Recently an attosecond angular streaking technique based on this semiclassical perspective has been widely applied to temporal measurement of the atomic and molecular dynamics in intense laser fields. We use a Wigner-distribution-like function to calculate the time-emission angle distribution, angular distribution and ionization time distribution for atomic ionization process in elliptically polarized few-cycle laser fields. By comparing with semiclassical calculations, we find that the two methods always show discrepancies except in some specific cases and the offset angles are generally not consistent with the offset times of the ionization time distributions obtained by the two methods even when the non-adiabatic effect is taken into account, indicating that the "attoclock" technique is in principle inaccurate. Moreover, calculations for linearly polarized laser fields also show similar discrepancies between two methods in the ionization time distribution. Our analysis indicates that the discrepancy between the semiclassical and quantum calculations can be attributed to correlation, i. e., temporal nonlocalization effect.

physics.atom-ph↗

arXiv2016

Momentum mapping of continuum electron wave packet interference

We analyze the two-dimensional photoelectrons momentum distribution of Ar atom ionized by midinfrared laser pulses and mainly concentrate on the energy range below 2Up. By using a generalized quantum trajectory Monte Carlo (GQTMC) simulation and comparing with the numerical solution of time-dependent Schrodinger equation (TDSE), we show that in the deep tunneling regime, the rescattered electron trajectories plays unimportant role and the interplay between the intracycle and inter-cycle results in a ring-like interference pattern. The ring-like interference pattern will mask the holographic interference structure in the low longitudinal momentum region. When the nonadiabatic tunneling contributes significantly to ionization, i.e., the Keldysh parameter 1, the contribution of the rescattered electron trajectories become large, thus holographic interference pattern can be clearly observed. Our results help paving the way for gaining physical insight into ultrafast electron dynamic process with attosecond temporal resolution.

physics.atom-ph↗

arXiv2016

Unraveling nonadiabatic ionization and Coulomb potential effects in strong-field photoelectron holography

Strong field photoelectron holography has been proposed as a means for interrogating the spatial and temporal information of electrons and ions in a dynamic system. After ionization, part of the electron wave packet may directly go to the detector (the reference wave), while another part may be driven back to the ion where it scatters off (the signal wave). The interference hologram of the two waves may be used to retrieve the target information. However, unlike conventional optical holography, the propagations of electron wave packets are affected by the Coulomb potential as well as by the laser field. In addition, electrons are emitted over the whole laser pulse duration, thus multiple interferences may occur. In this work, we used a generalized quantum-trajectory Monte Carlo method to investigate the effect of Coulomb potential and the nonadiabatic subcycle ionization on the photoelectron hologram. We showed that photoelectron hologram can be well described only when the nonadiabatic effect in ionization is accounted for, and Coulomb potential can be neglected only in the tunnel ionization regime. Our results help establishing photoelectron holography for probing spatial and dynamic properties of atoms and molecules.

physics.atom-ph↗

arXiv2016

Attosecond Interference Induced by Coulomb-Field-Driven Transverse Backward-Scattering Electron Wave-Packets

A novel and universal interference structure is found in the photoelectron momentum distribution of atoms in intense infrared laser field. Theoretical analysis shows that this structure can be attributed to a new form of Coulomb-field-driven backward-scattering of photoelectrons in the direction perpendicular to the laser field, in contrast to the conventional rescattering along the laser polarization direction. This transverse backward-scattering process is closely related to a family of photoelectrons initially ionized within a time interval of less than 200 attosecond around the crest of the laser electric field. Those electrons, acquiring near-zero return energy in the laser field, will be pulled back solely by the ionic Coulomb field and backscattered in the transverse direction. Moreover, this rescattering process mainly occurs at the first or the second return times, giving rise to different phases of the photoelectrons. The interference between these photoelectrons leads to unique curved interference fringes which are observable for most current intense field experiments, opening a new way to record the electron dynamics in atoms and molecules on a time scale much shorter than an optical cycle.

physics.atom-ph↗