UniFunc3D: Unified Active Spatial-Temporal Grounding for 3D Functionality Segmentation
Functionality segmentation in 3D scenes requires an agent to ground implicit natural-language instructions into precise masks of fine-grained interactive elements. Existing methods rely on fragmented pipelines that suffer from visual blindness during initial task parsing. We observe that these methods are limited by single-scale, passive and heuristic frame selection. We present UniFunc3D, a unified and training-free framework that treats the multimodal large language model as an active observer. By consolidating semantic, temporal, and spatial reasoning into a single forward pass, UniFunc3D performs joint reasoning to ground task decomposition in direct visual evidence. Our approach introduces active spatial-temporal grounding with a coarse-to-fine strategy. This allows the model to select correct video frames adaptively and focus on high-detail interactive parts while preserving the global context necessary for disambiguation. On SceneFun3D, UniFunc3D achieves state-of-the-art performance, surpassing both training-free and training-based methods by a large margin with a relative 59.9\% mIoU improvement, without any task-specific training. Code will be released on our project page: https://jiaying.link/unifunc3d.
69c37a2995b48f16a30078aa
Jiaying Lin
claimed_verified
2026-03-25T10:23:11.712Z
63932e755a2019fc85d66250
/avatars/f489439d9f9f2c0c4ebf2f95def63314.svg
Jiaying Lin
garrying
user
garrying
69c37a2995b48f16a30078ab
Dan Xu
UniFunc3D: Unified Active Spatial-Temporal Grounding for 3D Functionality Segmentation
Functionality segmentation in 3D scenes requires an agent to ground implicit natural-language instructions into precise masks of fine-grained interactive elements. Existing methods rely on fragmented pipelines that suffer from visual blindness during initial task parsing. We observe that these methods are limited by single-scale, passive and heuristic frame selection. We present UniFunc3D, a unified and training-free framework that treats the multimodal large language model as an active observer. By consolidating semantic, temporal, and spatial reasoning into a single forward pass, UniFunc3D performs joint reasoning to ground task decomposition in direct visual evidence. Our approach introduces active spatial-temporal grounding with a coarse-to-fine strategy. This allows the model to select correct video frames adaptively and focus on high-detail interactive parts while preserving the global context necessary for disambiguation. On SceneFun3D, UniFunc3D achieves state-of-the-art performance, surpassing both training-free and training-based methods by a large margin with a relative 59.9\% mIoU improvement, without any task-specific training. Code will be released on our project page: https://jiaying.link/unifunc3d.
UniFunc3D enables 3D scene functionality segmentation by treating multimodal large language models as active observers that perform joint semantic, temporal, and spatial reasoning through adaptive frame selection.
multimodal large language model functional segmentation 3D scenes task decomposition active spatial-temporal grounding coarse-to-fine strategy video frame selection joint reasoning mIoU