arXiv subjects

Delong Wu

Publications and source records attributed to Delong Wu.

4 recordsLinked to original sources

arXiv2026

Home3D 1.0: A High-Fidelity Image-to-3D Asset Generation System for Interior Design

We present Home3D 1.0, a modular image-to-3D generation system that produces high-quality 3D assets from a single reference image, targeting interior design and e-commerce applications. Given a photograph of a furniture or decor item, the system outputs a mesh with physically-based rendering (PBR) materials, and the mesh can be decomposed into material-specific components. The pipeline is organized into four tightly coupled modules: Geometry reconstructs a watertight mesh through latent SDF modelling with a geometry VAE and a coarse-to-fine flow-matching DiT; Texture predicts multiview albedo observations, reprojects them onto the mesh, and completes unseen surface regions with a 3D texture field; Material uses MatWeaver to obtain component masks through video-based segmentation and UV-space voting, then retrieves and bakes PBR maps from a curated material library through hierarchical multi-modal matching; and Parts generates material-editable semantic part meshes with a PartVAE and PartDiT, decoding multi-head part-specific SDF fields in one pass. Each module is evaluated independently with dedicated metrics, highlighting both the current system capability and the remaining gaps toward broader deployment.

cs.CV↗

arXiv2025

WIPES: Wavelet-based Visual Primitives

Pursuing a continuous visual representation that offers flexible frequency modulation and fast rendering speed has recently garnered increasing attention in the fields of 3D vision and graphics. However, existing representations often rely on frequency guidance or complex neural network decoding, leading to spectrum loss or slow rendering. To address these limitations, we propose WIPES, a universal Wavelet-based vIsual PrimitivES for representing multi-dimensional visual signals. Building on the spatial-frequency localization advantages of wavelets, WIPES effectively captures both the low-frequency "forest" and the high-frequency "trees." Additionally, we develop a wavelet-based differentiable rasterizer to achieve fast visual rendering. Experimental results on various visual tasks, including 2D image representation, 5D static and 6D dynamic novel view synthesis, demonstrate that WIPES, as a visual primitive, offers higher rendering quality and faster inference than INR-based methods, and outperforms Gaussian-based representations in rendering quality.

cs.CV↗

arXiv2024

Neural Poisson Solver: A Universal and Continuous Framework for Natural Signal Blending

Implicit Neural Representation (INR) has become a popular method for representing visual signals (e.g., 2D images and 3D scenes), demonstrating promising results in various downstream applications. Given its potential as a medium for visual signals, exploring the development of a neural blending method that utilizes INRs is a natural progression. Neural blending involves merging two INRs to create a new INR that encapsulates information from both original representations. A direct approach involves applying traditional image editing methods to the INR rendering process. However, this method often results in blending distortions, artifacts, and color shifts, primarily due to the discretization of the underlying pixel grid and the introduction of boundary conditions for solving variational problems. To tackle this issue, we introduce the Neural Poisson Solver, a plug-and-play and universally applicable framework across different signal dimensions for blending visual signals represented by INRs. Our Neural Poisson Solver offers a variational problem-solving approach based on the continuous Poisson equation, demonstrating exceptional performance across various domains. Specifically, we propose a gradient-guided neural solver to represent the solution process of the variational problem, refining the target signal to achieve natural blending results. We also develop a Poisson equation-based loss and optimization scheme to train our solver, ensuring it effectively blends the input INR scenes while preserving their inherent structure and semantic content. The lack of dependence on additional prior knowledge makes our method easily adaptable to various task categories, highlighting its versatility. Comprehensive experimental results validate the robustness of our approach across multiple dimensions and blending tasks.

cs.CV↗

arXiv2024

Spin order and dynamics in the topological rare-earth germanide semimetals

The $RE$Al(Si,Ge) ($RE$ = rare earth) family, known to break both the inversion- and time-reversal symmetries, represents one of the most suitable platforms for investigating the interplay between correlated-electron phenomena and topologically nontrivial bands. Here, we report on systematic magnetic, transport, and muon-spin rotation and relaxation ($μ$SR) measurements on (Nd,Sm)AlGe single crystals, which exhibit antiferromagnetic (AFM) transitions at $T_\mathrm{N} = 6.1$ and 5.9 K, respectively. In addition, NdAlGe undergoes also an incommensurate-to-commensurate ferrimagnetic transition at 4.5 K. Weak transverse-field $μ$SR measurements confirm the AFM transitions, featuring a $\sim$90 % magnetic volume fraction. In both cases, zero-field (ZF) $μ$SR measurements reveal a more disordered internal field distribution in NdAlGe than in SmAlGe, reflected in a larger transverse muon-spin relaxation rate $λ^\mathrm{T}$ at $T \ll T_\mathrm{N}$. This may be due to the complex magnetic structure of NdAlGe, which undergoes a series of metamagnetic transitions in an external magnetic field, while SmAlGe shows only a robust AFM order. In NdAlGe, the topological Hall effect (THE) appears between the first and the second metamagnetic transitions for $H \parallel c$, while it is absent in SmAlGe. Such THE in NdAlGe is most likely attributed to the field-induced topological spin textures. The longitudinal muon-spin relaxation rate $λ^\mathrm{L}(T)$, diverges near the AFM order, followed by a clear drop at $T < T_\mathrm{N}$. In the magnetically ordered state, spin fluctuations are significantly stronger in NdAlGe than in SmAlGe. In general, our longitudinal-field $μ$SR data indicate vigorous spin fluctuations in NdAlGe, thus providing valuable insights into the origin of THE and of the possible topological spin textures in $RE$Al(Si,Ge) Weyl semimetals.

cond-mat.str-el↗