Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prismatic Beams: Problem Solving

Prismatic Beams: Problem Solving

In the design of a supported timber beam subjected to a distributed load, both the beam's physical dimensions and the timber's characteristics, such as its grade and species, are critical. These factors determine the allowable stress values, which are crucial for calculating the necessary beam depth to ensure structural integrity and safety.
The design begins with analyzing the beam as a free body to identify moments and force balances, thereby determining support reactions. Next, the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Deep residual learning for molecular force fields.

Nature communications·2026

Same author

Qufeng Xuanbi Formula attenuates HDM-induced allergic asthma by targeting the STING/HIF-1α/glycolysis axis.

Journal of ethnopharmacology·2026

Same author

Intermittent fasting ameliorates Sjögren's syndrome-related dry eye through a preponderant bile acid-Akkermansia homeostasis establishment.

Journal of autoimmunity·2026

Same author

Correction: Ruan et al. Comparison of Extraction, Isolation, Purification, Structural Characterization and Immunomodulatory Activity of Polysaccharides from Two Species of <i>Cistanche</i>. <i>Molecules</i> 2025, <i>30</i>, 4754.

Molecules (Basel, Switzerland)·2026

Same author

RIPK3/SQSTM1-Mediated Necroptosis Activates the NLRP3 Inflammasome in Dry Eye Disease.

Investigative ophthalmology & visual science·2026

Same author

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 23, 2026

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

MuMA: 3D PBR Texturing via Multi-Channel Multi-View Generation and Albedo Post-Processing.

Lingting Zhu, Jingrui Ye, Runze Zhang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|April 21, 2026

Summary

This summary is machine-generated.

This study introduces MuMA, a novel method for 3D physically based rendering (PBR) texturing. MuMA enhances material modeling by using multi-channel, multi-view generation and albedo post-processing for superior visual quality.

More Related Videos

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology

Published on: December 19, 2010

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Related Experiment Videos

Last Updated: Apr 23, 2026

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology

Published on: December 19, 2010

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Area of Science:

Computer Graphics
Artificial Intelligence

Background:

Current 3D generation methods struggle with physically based rendering (PBR) texturing due to data limitations and complex multi-channel material modeling.
Existing techniques often fail to achieve high fidelity in representing diverse material properties.

Purpose of the Study:

To introduce MuMA, a novel method for advanced 3D PBR texturing.
To overcome limitations in current 3D generation techniques for realistic material representation.

Main Methods:

MuMA employs Multi-channel Multi-view generation and Albedo post-processing for 3D PBR texturing.
The method models shaded and albedo appearance channels, integrating intrinsic decomposition for material properties.
Multimodal large language models are utilized to emulate artist-driven material assessment and selection.

Main Results:

MuMA demonstrates superior performance in visual quality compared to existing 3D texturing methods.
The proposed approach achieves enhanced material fidelity in generated 3D assets.
Experiments validate the effectiveness of the integrated intrinsic decomposition and LLM-based material emulation.

Conclusions:

MuMA offers a significant advancement in 3D PBR texturing, addressing key challenges in material modeling.
The method provides a more robust and visually accurate approach to generating realistic 3D materials.
Future work can explore further integration of AI for automated material creation and refinement.