We introduce Alterbute, a diffusion-based method for editing an object's intrinsic attributes in an image. We allow changing color, texture, material, and even the shape of an object, while preserving its perceived identity and scene context. Existing approaches either rely on unsupervised priors that often fail to preserve identity or use overly restrictive supervision that prevents meaningful intrinsic variations. Our method relies on: (i) a relaxed training objective that allows the model to change both intrinsic and extrinsic attributes conditioned on an identity reference image, a textual prompt describing the target intrinsic attributes, and a background image and object mask defining the extrinsic context. At inference, we restrict extrinsic changes by reusing the original background and object mask, thereby ensuring that only the desired intrinsic attributes are altered; (ii) Visual Named Entities (VNEs) - fine-grained visual identity categories (e.g., ''Porsche 911 Carrera'') that group objects sharing identity-defining features while allowing variation in intrinsic attributes. We use a vision-language model to automatically extract VNE labels and intrinsic attribute descriptions from a large public image dataset, enabling scalable, identity-preserving supervision. Alterbute outperforms existing methods on identity-preserving object intrinsic attribute editing.

翻译：我们提出Alterbute，一种基于扩散模型的方法，用于编辑图像中物体的固有属性。我们允许改变物体的颜色、纹理、材质甚至形状，同时保留其感知到的身份特征和场景上下文。现有方法要么依赖无监督先验（这通常无法保持身份特征），要么使用过于严格的监督（这阻碍了有意义的固有属性变化）。我们的方法依赖于：（i）一个松弛的训练目标，该目标允许模型在身份参考图像、描述目标固有属性的文本提示以及定义外部上下文背景图像和物体掩码的联合约束下，改变物体的固有属性和外部属性。在推理时，我们通过复用原始背景和物体掩码来限制外部变化，从而确保只改变所需的固有属性；（ii）视觉命名实体（VNEs）——细粒度视觉身份类别（例如“保时捷911 Carrera”），这些类别将共享身份特征但允许固有属性变化的物体分组。我们使用视觉语言模型从大规模公共图像数据集中自动提取VNE标签和固有属性描述，从而实现可扩展的、保持身份特征的监督。Alterbute在保持身份特征的物体固有属性编辑任务上优于现有方法。