Boosting Zero-Shot 3D Style Transfer with 2D Pre-trained Priors

2026-05-28 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

The Data-Sufficient StyleGaussian (DS-StyleGaussian) model introduces a novel approach to zero-shot 3D style transfer, addressing the significant challenge of data scarcity in 3D stylization. Traditional 3D style transfer methods often suffer from limited content images, as models are typically trained on single scenes, hindering performance due to insufficient content-style image pairs for supervision. DS-StyleGaussian integrates a decoder pre-trained on extensive 2D image datasets into its 3D style transfer pipeline. This integration effectively leverages the rich prior knowledge acquired from learning over numerous 2D content-style pairs. The method combines feature Gaussian splatting with deferred stylization, ensuring both high-quality stylization and multi-view consistency by unifying view-dependent operations into a view-invariant process. Experiments confirm that DS-StyleGaussian surpasses existing zero-shot 3D style transfer techniques in visual quality across diverse datasets, highlighting the potential of 2D pre-training to enhance 3D tasks and bridge the data gap.

Key takeaway

For Computer Vision Engineers developing 3D content generation pipelines, consider integrating 2D pre-trained decoders into your 3D style transfer workflows. This approach directly addresses data scarcity issues common in 3D tasks, enabling superior visual quality and multi-view consistency compared to traditional methods. By leveraging extensive 2D prior knowledge, you can significantly enhance stylization performance and bridge the data gap between 2D and 3D domains.

Key insights

Integrating 2D pre-trained decoders into 3D style transfer pipelines effectively mitigates data scarcity and enhances stylization quality.

Principles

• 2D pre-training boosts 3D task performance.
• Data scarcity limits 3D stylization.
• View-invariant processes ensure consistency.

Method

DS-StyleGaussian integrates a 2D pre-trained decoder with feature Gaussian splatting and deferred stylization. This unifies view-dependent operations into a view-invariant process, ensuring multi-view consistent, high-quality 3D stylization.

In practice

• Apply 2D pre-trained models to 3D tasks.
• Use Gaussian splatting for 3D scene representation.
• Implement deferred stylization for consistency.

Topics

• Zero-Shot 3D Style Transfer
• 2D Pre-trained Priors
• Gaussian Splatting
• Data Scarcity
• Computer Vision
• Deferred Stylization

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.