{"type":"ScholarlyArticle","title":"Open-Vocabulary Object 6D Pose Estimation via Modulated Textual Semantics","authors":[{"name":"Zixuan Sun","slug":"zixuan-sun","given":"Zixuan","family":"Sun","affiliation":""},{"name":"Hui Shuai","slug":"hui-shuai","given":"Hui","family":"Shuai","affiliation":""},{"name":"Qingshan Liu","slug":"qingshan-liu","given":"Qingshan","family":"Liu","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/186","url":"https://ijcai.org/proceedings/2026/186","pdf":"https://ijcai.org/proceedings/2026/0186.pdf","markdown":"https://ijcai.org/proceedings/2026/186.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/186","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":186,"pages":{"first":1667,"last":1675},"keywords":["Computer Vision: Scene analysis and understanding","Computer Vision: 3D computer vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}