Coarse architectural models are often generated at scales ranging from
individual buildings to scenes for downstream applications such as Digital Twin
City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as
twins from 3D dense reconstructions. However, these models typically lack
realistic texture relative to the real building or scene, making them
unsuitable for vivid display or direct reference. In this paper, we present
TwinTex, the first automatic texture mapping framework to generate a
photo-realistic texture for a piece-wise planar proxy. Our method addresses
most challenges occurring in such twin texture generation. Specifically, for
each primitive plane, we first select a small set of photos with greedy
heuristics considering photometric quality, perspective quality and facade
texture completeness. Then, different levels of line features (LoLs) are
extracted from the set of selected photos to generate guidance for later steps.
With LoLs, we employ optimization algorithms to align texture with geometry
from local to global. Finally, we fine-tune a diffusion model with a multi-mask
initialization component and a new dataset to inpaint the missing region.
Experimental results on many buildings, indoor scenes and man-made objects of
varying complexity demonstrate the generalization ability of our algorithm. Our
approach surpasses state-of-the-art texture mapping methods in terms of
high-fidelity quality and reaches a human-expert production level with much
less effort. Project page: this https URL

本文介绍了一种用于生成分段平面代理的自动纹理映射框架 TwinTex，通过选择一组照片并提取线特征，利用优化算法将纹理与几何对齐，并使用扩散模型来修复缺失区域，实现了高保真质量的纹理贴图。

TwinTex: 面向抽象 3D 建筑模型的几何感知纹理生成

TwinTex: Geometry-aware Texture Generation for Abstracted 3D  Architectural Models

4D reconstruction and rendering of human activities is critical for immersive
VR/AR experience.Recent advances still fail to recover fine geometry and
texture results with the level of detail present in the input images from
sparse multi-view RGB cameras. In this paper, we propose NeuralHumanFVV, a
real-time neural human performance capture and rendering system to generate
both high-quality geometry and photo-realistic texture of human activities in
arbitrary novel views. We propose a neural geometry generation scheme with a
hierarchical sampling strategy for real-time implicit geometry inference, as
well as a novel neural blending scheme to generate high resolution (e.g., 1k)
and photo-realistic texture results in the novel views. Furthermore, we adopt
neural normal blending to enhance geometry details and formulate our neural
geometry and texture rendering into a multi-task learning framework. Extensive
experiments demonstrate the effectiveness of our approach to achieve
high-quality geometry and photo-realistic free view-point reconstruction for
challenging human performances.

本文提出了一种名为 NeuralHumanFVV 的实时神经人体表演捕捉和渲染系统，采用分层采样策略以及新颖的神经混合方案，能够在新视角下生成高质量的几何和逼真的贴图结果，实现了高质量的四维重建和逼真的自由视点重建。