mlsanityData to 7 Oct 2026

Spatial and Visual Perspective-Taking via View Rotation and Relation Reasoning for Embodied Reference Understanding

arXiv 2309.01073 ↗First posted 3 Sep 2023cs.CVcited by 10

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Abstract (arXiv)

Embodied Reference Understanding studies the reference understanding in an embodied fashion, where a receiver is required to locate a target object referred to by both language and gesture of the sender in a shared physical environment. Its main challenge lies in how to make the receiver with the egocentric view access spatial and visual information relative to the sender to judge how objects are oriented around and seen from the sender, i.e., spatial and visual perspective-taking. In this paper, we propose a REasoning from your Perspective (REP) method to tackle the challenge by modeling relations between the receiver and the sender and the sender and the objects via the proposed novel view rotation and relation reasoning. Specifically, view rotation first rotates the receiver to the position of the sender by constructing an embodied 3D coordinate system with the position of the sender as the origin. Then, it changes the orientation of the receiver to the orientation of the sender by encoding the body orientation and gesture of the sender. Relation reasoning models the nonverbal and verbal relations between the sender and the objects by multi-modal cooperative reasoning in gesture, language, visual content, and spatial position. Experiment results demonstrate the effectiveness of REP, which consistently surpasses all existing state-of-the-art algorithms by a large margin, i.e., +5.22% absolute accuracy in terms of Prec0.5 on YouRefIt.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 42 of its 55 references that are in the map. First posted on arXiv 3 Sep 2023.

Builds on

42 of 55 references.

References · 42

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30 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 4; 2026: 3.

10 papers.

Similar, not cited

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  1. 2025SpinBench: Perspective and Rotation as a Lens on Spatial Reasoning in VLMs
  2. 2025REM: Evaluating LLM Embodied Spatial Reasoning through Multi-Frame Trajectories
  3. 2025Towards Embodied Cognition in Robots via Spatially Grounded Synthetic Worlds
  4. 2026Contextual Observer Grounding: Evaluating Situated Spatial Reasoning in Vision-Language Models
  5. 2026Egocentric Bias in Vision-Language Models
  6. 2025EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks
  7. 2026Cognitively-Inspired Tokens Overcome Egocentric Bias in Multimodal Models
  8. 2025Beyond Recognition: Evaluating Visual Perspective Taking in Vision Language Models