Generating Multi-view Consistent Textures with Collaborative Control

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A new research paper published on arXiv proposes an end-to-end pipeline for generating physically-based rendering (PBR) textures from a text description. This pipeline leverages diffusion models, which are a type of artificial intelligence that learns to generate images from a given dataset.

Existing methods for generating textures from text prompts often struggle to produce consistent textures across multiple viewpoints of a 3D object. The paper’s authors introduce a novel method called Collaborative Control that addresses this issue. Collaborative Control is a technique that directly models the distribution of PBR images, including normal bump maps, which capture the surface details of an object.

The authors demonstrate that their approach can generate high-quality PBR textures that are consistent enough to be naively merged into a single texture map, which is the standard format for representing textures in graphics applications. The authors conducted extensive ablation studies to understand the importance of different components of their proposed pipeline. The study reveals that the following components are crucial for achieving consistent multi-view textures:

• Pixel-wise correspondences: This component allows the model to learn how features in different viewpoints of an object correspond to each other. This is essential for generating seamless textures that don’t exhibit any ghosting artifacts when merged into a single texture map.

• Occlusion awareness: This component ensures that the model correctly handles occlusions, which occur when certain parts of an object are hidden from view. This is important to avoid leaking features from the reference view into occluded areas.

• Reference view attention: This component ensures that the model correctly incorporates the information from a reference view into the generated textures. This is particularly important for generating consistent textures across viewpoints that significantly differ from the reference view.

• DINOv2 feature attention: This component enhances the semantic consistency of the generated textures across multiple viewpoints by leveraging the DINOv2 features of the reference view. This is particularly important for capturing subtle stylistic details that may not be visible in the reference view.

The proposed method offers several advantages over existing techniques for generating PBR textures from text prompts. It produces high-quality textures that are multi-view consistent and can be easily merged into a single texture map. This approach also provides a powerful way to control the generation process by leveraging a variety of input conditions, such as the text prompt, the object geometry, and the reference view.

However, the authors acknowledge that their approach is still under development and has some limitations. For example, their method doesn’t effectively handle occlusions in all cases, and it may still struggle to generate textures with the same level of realism as some of the best existing approaches. The authors believe that future research will address these limitations and further improve the quality and efficiency of PBR texture generation from text prompts.

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