解决文本生成图像时身份失真问题，保持姿态与结构一致
Identity-preserving Distillation Sampling by Fixed-Point Iterator
- 通过固定点迭代正则化修正得分函数，抑制错误梯度
- 在图像编辑和可编辑NeRF中实现清晰、无模糊的身份保留
- 适合需要精确控制人物或物体结构的生成任务
Score distillation sampling (SDS) 在文本条件的2D图像和3D物体生成中展现出强大能力，但常因噪声梯度导致图像模糊。尽管使用参考对调整偏置可缓解这一问题，去偏技术仍受错误梯度影响。为此，本文提出身份保留型蒸馏采样（IDS），通过分析发现误差源于文本条件得分，提出固定点迭代正则化（FPR）技术，直接修改得分函数，确保身份（包括姿态与结构）的持续保留。FPR实现自我修正，使生成结果在图像到图像编辑及可编辑神经辐射场（NeRF）中更清晰、明确，且源图与编辑后数据的结构一致性显著优于当前最先进方法。
原文摘要 · Abstract (English)
Score distillation sampling (SDS) demonstrates a powerful capability for text-conditioned 2D image and 3D object generation by distilling the knowledge from learned score functions. However, SDS often suffers from blurriness caused by noisy gradients. When SDS meets the image editing, such degradations can be reduced by adjusting bias shifts using reference pairs, but the de-biasing techniques are still corrupted by erroneous gradients. To this end, we introduce Identity-preserving Distillation Sampling (IDS), which compensates for the gradient leading to undesired changes in the results. Based on the analysis that these errors come from the text-conditioned scores, a new regularization technique, called fixed-point iterative regularization (FPR), is proposed to modify the score itself, driving the preservation of the identity even including poses and structures. Thanks to a self-correction by FPR, the proposed method provides clear and unambiguous representations corresponding to the given prompts in image-to-image editing and editable neural radiance field (NeRF). The structural consistency between the source and the edited data is obviously maintained compared to other state-of-the-art methods.
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