---
title: "Open-Vocabulary Object 6D Pose Estimation via Modulated Textual Semantics"
authors:
  - "Zixuan Sun"
  - "Hui Shuai"
  - "Qingshan Liu"
doi: "10.24963/ijcai.2026/186"
url: "https://ijcai.org/proceedings/2026/186"
pdf: "https://ijcai.org/proceedings/2026/0186.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "1667-1675"
track: "Main Track"
keywords:
  - "Computer Vision: Scene analysis and understanding"
  - "Computer Vision: 3D computer vision"
---

# Open-Vocabulary Object 6D Pose Estimation via Modulated Textual Semantics

Zixuan Sun, Hui Shuai, Qingshan Liu

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 1667-1675. DOI: [10.24963/ijcai.2026/186](https://doi.org/10.24963/ijcai.2026/186)

## Abstract

Estimating the 6D pose of novel objects without CAD models or video sequences remains a challenging problem. Recent works explore text-driven approaches to address this challenge in an open-vocabulary manner. However, these methods typically treat text embeddings as static priors, which lack the flexibility to adapt to specific visual scenes, thereby limiting reliable cross-view correspondences. In this paper, we propose a framework that modulates static textual embeddings into adaptable semantic guidance for RGB-D open-vocabulary 6D pose estimation. Specifically, we introduce a Text Feature Modulation (TFM) module that uses learnable queries to transform fixed text embeddings into adaptive semantic representations. These representations are explicitly calibrated by a Foreground-Grounded Semantic Alignment (FGSA) strategy, which suppresses background distractions via contrastive learning. Furthermore, we design a Semantic-Anchored Cross-view Fusion (SACF) module that aggregates image features from the reference and query views using modulated semantics as an invariant anchor. Finally, we predict object segmentation and establish semantic-aware correspondences, leveraging modulated textual semantics to filter mismatches for robust 6D pose estimation. Extensive experiments on standard benchmarks demonstrate that our method achieves superior performance.