DICArt: Advancing Category-level Articulated Object Pose Estimation in Discrete State-Spaces
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arXiv
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| Format: | Preprint |
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2026
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| author | Zhang, Li Mei, Mingyu Wang, Ailing Meng, Xianhui Zhong, Yan Song, Xinyuan Liu, Liu Wang, Rujing He, Zaixing Lu, Cewu |
| author_facet | Zhang, Li Mei, Mingyu Wang, Ailing Meng, Xianhui Zhong, Yan Song, Xinyuan Liu, Liu Wang, Rujing He, Zaixing Lu, Cewu |
| contents | Articulated object pose estimation is a core task in embodied AI. Existing methods typically regress poses in a continuous space, but often struggle with 1) navigating a large, complex search space and 2) failing to incorporate intrinsic kinematic constraints. In this work, we introduce DICArt (DIsCrete Diffusion for Articulation Pose Estimation), a novel framework that formulates pose estimation as a conditional discrete diffusion process. Instead of operating in a continuous domain, DICArt progressively denoises a noisy pose representation through a learned reverse diffusion procedure to recover the GT pose. To improve modeling fidelity, we propose a flexible flow decider that dynamically determines whether each token should be denoised or reset, effectively balancing the real and noise distributions during diffusion. Additionally, we incorporate a hierarchical kinematic coupling strategy, estimating the pose of each rigid part hierarchically to respect the object's kinematic structure. We validate DICArt on both synthetic and real-world datasets. Experimental results demonstrate its superior performance and robustness. By integrating discrete generative modeling with structural priors, DICArt offers a new paradigm for reliable category-level 6D pose estimation in complex environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19565 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DICArt: Advancing Category-level Articulated Object Pose Estimation in Discrete State-Spaces Zhang, Li Mei, Mingyu Wang, Ailing Meng, Xianhui Zhong, Yan Song, Xinyuan Liu, Liu Wang, Rujing He, Zaixing Lu, Cewu Computer Vision and Pattern Recognition Artificial Intelligence Articulated object pose estimation is a core task in embodied AI. Existing methods typically regress poses in a continuous space, but often struggle with 1) navigating a large, complex search space and 2) failing to incorporate intrinsic kinematic constraints. In this work, we introduce DICArt (DIsCrete Diffusion for Articulation Pose Estimation), a novel framework that formulates pose estimation as a conditional discrete diffusion process. Instead of operating in a continuous domain, DICArt progressively denoises a noisy pose representation through a learned reverse diffusion procedure to recover the GT pose. To improve modeling fidelity, we propose a flexible flow decider that dynamically determines whether each token should be denoised or reset, effectively balancing the real and noise distributions during diffusion. Additionally, we incorporate a hierarchical kinematic coupling strategy, estimating the pose of each rigid part hierarchically to respect the object's kinematic structure. We validate DICArt on both synthetic and real-world datasets. Experimental results demonstrate its superior performance and robustness. By integrating discrete generative modeling with structural priors, DICArt offers a new paradigm for reliable category-level 6D pose estimation in complex environments. |
| title | DICArt: Advancing Category-level Articulated Object Pose Estimation in Discrete State-Spaces |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2602.19565 |