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About this paper
Yushen Zuo, Jun Xiao, Kin-Chung Chan, Rongkang Dong, Cuixin Yang, Zongqi He, Hao Xie, Kin-Man Lam
Researchers from The Hong Kong Polytechnic University developed OSDiffST, a method for multi-view consistent style transfer for 3D scenes, leveraging a one-step diffusion model and vision conditioning. The approach achieves superior visual quality and multi-view consistency across viewpoints without explicit 3D information, while maintaining fast inference speeds comparable to CNN-based methods.
Key insights
- Multi-view consistent style transfer for 3D scenes can be achieved effectively using only 2D multi-view images by implicitly preserving structural information via a dedicated image structure loss and a strong generative prior.
- One-step diffusion models, when combined with efficient adaptation techniques like LoRA and a well-designed vision conditioning module, can provide state-of-the-art results in complex image generation tasks with high efficiency.
- A vision-language projector, using a pre-trained CLIP encoder, offers an efficient and intuitive way to extract and inject style from reference images, effectively guiding the diffusion process.
Summary by alphaXiv.
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