Assistant Placement Aria: A Benchmark for Egocentric Placement Assistance

2026-08-01

AI Summary

This work addresses the limitation of existing virtual placement methods that predict only a single predefined location by proposing a new paradigm that identifies all plausible target positions satisfying both scene context and human-centric constraints. To support this paradigm, we introduce the first multimodal benchmark dataset for virtual placement, encompassing both synthetic and real indoor scenes and covering three subtasks: 2D panel placement, seating suggestion, and TV placement, with aligned 2D images, 3D point clouds, and textual descriptions. This benchmark uniquely integrates global scene layout, local object affordances, and human intent constraints, filling a critical gap in standardized datasets for the field. Experimental evaluation reveals performance bottlenecks of current foundation models on human-centric placement tasks, establishing a standardized platform for future research.

Abstract

Human assistance in robotics spans around several tasks such as navigation, object manipulation, and placement, where a key challenge is selecting target destinations that align with human intentions or preferences. We focus on this challenge in the context of Virtual Placement (VP), the task of identifying all plausible target locations given scene context and human-centric constraints. This differs from traditional placement tasks that typically focus on a single, predefined target location. The VP problem is complex, as it requires both global and local reasoning about the scene's geometry, semantics, and plausibility. To address this gap, we introduce {\bf Assistant Placement Aria}, the first benchmark to explore diverse aspects of VP, including global, local, and human-centric constraints. It contains both synthetic and real indoor scenes annotated for three tasks: (i)~2D Panel Placement, (ii)~Sitting Suggestion, and (iii)~TV Placement. Each scene includes 2D images, a 3D point cloud, and a textual description of the objects within the scene. By contributing this benchmark, we aim to encourage further research in this underexplored and challenging field that is critically dependent on relevant data. We also evaluate several foundation models for object detection and segmentation on our benchmark.

Problem

• Virtual Placement
• egocentric placement
• human-centric constraints
• scene understanding
• placement assistance

Innovation

• Virtual Placement
• Egocentric Benchmark
• Human-Centric Constraints
• Multi-Modal Scene Understanding
• Plausible Target Localization