We present Piva (Preserving Identity with Variational Score Distillation), a
novel optimization-based method for editing images and 3D models based on
diffusion models. Specifically, our approach is inspired by the recently
proposed method for 2D image editing - Delta Denoising Score (DDS). We pinpoint
the limitations in DDS for 2D and 3D editing, which causes detail loss and
over-saturation. To address this, we propose an additional score distillation
term that enforces identity preservation. This results in a more stable editing
process, gradually optimizing NeRF models to match target prompts while
retaining crucial input characteristics. We demonstrate the effectiveness of
our approach in zero-shot image and neural field editing. Our method
successfully alters visual attributes, adds both subtle and substantial
structural elements, translates shapes, and achieves competitive results on
standard 2D and 3D editing benchmarks. Additionally, our method imposes no
constraints like masking or pre-training, making it compatible with a wide
range of pre-trained diffusion models. This allows for versatile editing
without needing neural field-to-mesh conversion, offering a more user-friendly
experience.

利用扩散模型，提出了一种名为 Piva（保留身份的变分得分馏分）的新颖的基于优化的图像和三维模型编辑方法，旨在解决已有方法中存在的细节丢失和过饱和等问题，通过额外的得分馏分项保持了原有特征，成功地改变视觉属性、添加结构元素、转换形状，并在标准的 2D 和 3D 编辑基准上取得了竞争性结果。