MAOAM: Unified Object and Material Selection with Vision-Language Models

摘要

Selection is a core operation in interactive image editing, enabling tasks such as composition or manipulation. To be practically useful, a user should be able to specify and disambiguate the desired selection region through either text- or click-based interactions, and the system should support selecting not only objects but also other criteria, such as materials. Material-based selection can be particularly valuable for tasks like re-texturing surfaces or consistently editing all instances of a specific material in a scene. However, existing vision–language-model (VLM) based selection methods are largely object-centric and typically support only a single interaction modality, limiting their applicability in real editing workflows. In this work, we thus present Mask Any Object And Material (MAOAM), a unified selection framework that enables precise object- and material-level selection across both text- and click-based interactions. MAOAM leverages a VLM with a segmentation head to produce pixel-accurate masks from user prompts: the VLM interprets the user’s selection intent — object- or material-level — and encodes visual entities, attributes, and spatial relations, while the segmentation head decodes the VLM’s output token into a mask. A key challenge is that material selection datasets with text annotations are unavailable. We therefore propose a scalable data generation pipeline: we collect real and synthetic images with material masks, then leverage VLMs to generate material descriptions with rich visual-semantic information. Using the generated data, we train MAOAM with a multi-task objective over click- and text-based selection, along with an auxiliary VQA task derived from the material descriptions to facilitate deeper material understanding. Despite being trained with uni-modal prompts, our model exhibits an emergent improvement in selection quality when combining text and clicks at inference time, enabling more flexible image editing workflows. Experiments demonstrate accurate and coherent selections across diverse objects, materials, and interaction scenarios, highlighting robustness in practice.