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：我们提出了Alterbute，一种基于扩散模型的图像物体固有属性编辑方法。该方法允许改变物体的颜色、纹理、材质乃至形状，同时保持其感知身份与场景上下文。现有方法要么依赖通常无法保持身份的无监督先验，要么采用过度严格的监督从而阻碍有意义的固有属性变化。我们的方法基于：（i）一种宽松的训练目标，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的背景图像和物体掩码的条件下，同时改变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，从而确保仅改变所需的固有属性；（ii）视觉命名实体——细粒度的视觉身份类别（例如“保时捷911 Carrera”），将共享身份定义特征但允许固有属性变化的物体进行分组。我们利用视觉-语言模型从大型公共图像数据集中自动提取VNE标签和固有属性描述，实现了可扩展的、保持身份的监督。Alterbute在保持身份的物体固有属性编辑任务上优于现有方法。