Radial Based Analysis of GRNN in Non-Textured Image Inpainting

Deep Generative Model for Image Inpainting with Local Binary Pattern Learning and Spatial Attention

Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting. The DL-based image inpainting approaches can produce visually plausible results, but often generate various unpleasant artifacts, especially in the boundary and highly textured regions. To tackle this challenge, in this work, we propose a new end-to-end, two-stage (coarse-to-fine) generative model through combining a local binary pattern (LBP) learning network with an actual inpainting network. Specifically, the first LBP learning network using U-Net architecture is designed to accurately predict th…

基于掩码的体素扩散用于联合几何和颜色修复

Mask-Conditioned Voxel Diffusion for Joint Geometry and Color Inpainting

本文提出基于掩码的体素扩散方法，用于修复受损3D物体的几何和颜色，通过两阶段框架实现更完整和一致的修复效果。

Text2Room：从2D文本到图像模型中提取带纹理的3D网格

Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models

Text2Room利用预训练2D文本到图像模型和单目深度估计，通过定制视点选择和连续对齐策略，从文本生成房间规模带纹理3D网格，是首个仅凭文本生成完整3D场景的方法。

CaPa：用于高效生成4K带纹理网格的Carve-n-Paint合成方法

CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation

针对现有3D生成算法多视角不一致、速度慢、保真度低等问题，提出两阶段CaPa框架，解耦几何生成与纹理合成，结合空间解耦注意力和3D感知遮挡修复，可30秒内生成高保真4K带纹理3D资产，性能优于现有方法。

基于场潜变量的单网格扩散模型纹理生成

Single Mesh Diffusion Models with Field Latents for Texture Generation

提出直接在3D表面操作的场潜变量扩散模型框架，通过离散向量场编码纹理并学习去噪，实现高质量单网格纹理生成、编辑与迁移。

xCloth: Extracting Template-free Textured 3D Clothes from a Monocular Image

Existing approaches for 3D garment reconstruction either assume a predefined template for the garment geometry (restricting them to fixed clothing styles) or yield vertex colored meshes (lacking high-frequency textural details). Our novel framework co-learns geometric and semantic information of garment surface from the input monocular image for template-free textured 3D garment digitization. More specifically, we propose to extend PeeledHuman representation to predict the pixel-aligned, layered depth and semantic maps to extract 3D garments. The layered representation is further exploited to …