Generating Precise Surface Materials for 3D Assets using 2D Material Priors

Concetti Chiave

Leveraging 2D material prior knowledge to automatically generate precise surface materials for 3D assets.

Sintesi

The paper proposes a novel workflow called MaterialSeg3D that can generate precise surface materials for 3D assets by utilizing 2D material prior knowledge.
Key highlights:

Existing 3D asset generation methods often bake illumination effects into the texture, leading to entangled material maps that cannot be realistically re-lit in novel scenes.
The authors construct the Materialized Individual Objects (MIO) dataset, a large-scale 2D single-object material segmentation dataset with diverse camera angles and accurate annotations.
MaterialSeg3D takes a 3D asset's geometry mesh and albedo UV as input, renders multi-view images, and uses a material segmentation model trained on MIO to predict and fuse material labels into a final UV map.
The final material UV map is then converted into a PBR material UV map with metallic and roughness values.
Extensive experiments demonstrate the effectiveness of the proposed approach in generating high-quality, realistic surface materials for 3D assets, outperforming previous methods.

Statistiche

The MIO dataset contains 23,062 multi-view images of individual complex objects, annotated into 14 material classes.
The dataset includes approximately 4,000 top-view images, providing a unique perspective rarely found in existing 2D datasets.

Citazioni

"Driven by powerful image diffusion models, recent research has achieved the automatic creation of 3D objects from textual or visual guidance. By performing score distillation sampling (SDS) iteratively across different views, these methods succeed in lifting 2D generative prior to the 3D space."
"However, such a 2D generative image prior bakes the effect of illumination and shadow into the texture. As a result, material maps optimized by SDS inevitably involve spurious correlated components."

Approfondimenti chiave tratti da

MaterialSeg3D: Segmenting Dense Materials from 2D Priors for 3D Assets

by Zeyu Li,Ruit... alle arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13923.pdf

Domande più approfondite

How can the proposed MaterialSeg3D workflow be extended to handle 3D assets with complex geometry and topology?

The MaterialSeg3D workflow can be extended to handle 3D assets with complex geometry and topology by incorporating advanced mesh processing techniques. One approach is to integrate mesh refinement algorithms that can handle intricate geometries and topologies, ensuring that the material information is accurately mapped onto the surfaces of the assets. Additionally, implementing advanced UV unwrapping methods can help optimize the mapping of material labels onto complex surfaces, ensuring that the PBR material information is applied seamlessly across the asset. Furthermore, incorporating geometric feature extraction algorithms can enhance the segmentation of material regions on complex surfaces, improving the accuracy of the material generation process for such assets.

What are the potential limitations of using 2D material prior knowledge for 3D asset material generation, and how can they be addressed?

One potential limitation of using 2D material prior knowledge for 3D asset material generation is the domain gap between 2D images and 3D assets, which can lead to inaccuracies in material prediction. This limitation can be addressed by augmenting the dataset with a diverse range of 3D asset renderings to bridge the domain gap and improve the generalization of the material segmentation model. Additionally, incorporating domain adaptation techniques can help align the distributions of 2D images and 3D assets, reducing the impact of the domain gap on material generation. Furthermore, leveraging self-supervised learning methods can enhance the model's ability to learn robust features from both 2D and 3D data, improving the accuracy of material predictions for 3D assets.

How can the generated PBR material information be further utilized in downstream applications, such as realistic rendering or material-aware 3D editing?

The generated PBR material information can be further utilized in downstream applications for realistic rendering and material-aware 3D editing by integrating it into rendering engines that support Physically-Based Rendering (PBR). By incorporating the PBR material maps into the rendering pipeline, the assets can be rendered with accurate material properties, leading to photorealistic visual effects. Additionally, the PBR material information can be used in material-aware 3D editing tools to enable artists and designers to manipulate the material properties of 3D assets with precision. This allows for interactive material editing, such as adjusting metallic or roughness values, and facilitates the creation of visually compelling and realistic 3D scenes.