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About this paper
SeonHwa Kim, Jiwon Kim, Soobin Park, Donghoon Ahn, Jiwon Kang, Seungryong Kim, Kyong Hwan Jin, Eunju Cha
Identity-preserving Distillation Sampling (IDS) introduces a method to precisely maintain the structural identity of source content during text-guided image and 3D NeRF editing. It utilizes Fixed-Point Iterative Regularization (FPR) to explicitly correct text-conditioned scores, leading to higher fidelity edits and improved structural consistency compared to existing techniques.
Key insights
- The fundamental cause of identity loss in SDS-based methods is the imprecise representation of the source image by its text-conditioned score, causing 'inherent errors' in gradient calculations.
- Iteratively refining the noisy source latent to align its posterior mean with the original source image can correct the text-conditioned score, making it a reliable proxy for identity preservation.
- By using this corrected source score in a distillation sampling framework, identity-preserving gradients can be generated, ensuring structural consistency across diverse editing tasks.
Summary by alphaXiv.
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