R^2-Art: Category-Level Articulation Pose Estimation from Single RGB Image via Cascade Render Strategy
Li Zhang, Haonan Jiang, Yukang Huo, Yan Zhong, Jianan Wang, Xue Wang, Rujing Wang, Liu Liu
Abstract
Human life is filled with articulated objects. Previous works for estimating the pose of category-level articulated objects rely on costly 3D point clouds or RGB-D images. In this paper, our goal is to estimate category-level articulation poses from a single RGB image, where we propose R2-Art, a novel category-level Articulation pose estimation framework from a single RGB image and a cascade Render strategy. Given an RGB image as input, R2-Art estimates per-part 6D pose for the articulation. Specifically, we design parallel regression branches tailored to generate camera-to-root translation and rotation. Using the predicted joint states, we perform PC prior transformation and deformation with a joint-centric modeling approach. For further refinement, a cascade render strategy is proposed for projecting the 3D deformed prior onto the 2D mask. Extensive experiments are provided to validate our R2-Art on various datasets ranging from synthetic datasets to real-world scenarios, demonstrating the superior performance and robustness of the R2-Art. We believe that this work has the potential to be applied in many fields including robotics, embodied intelligence, and augmented reality.
BibTeX
@article{Zhang_Jiang_Huo_Zhong_Wang_Wang_Wang_Liu_2025, title={R^2-Art: Category-Level Articulation Pose Estimation from Single RGB Image via Cascade Render Strategy}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33083}, DOI={10.1609/aaai.v39i9.33083}, abstractNote={Human life is filled with articulated objects. Previous works for estimating the pose of category-level articulated objects rely on costly 3D point clouds or RGB-D images. In this paper, our goal is to estimate category-level articulation poses from a single RGB image, where we propose R2-Art, a novel category-level Articulation pose estimation framework from a single RGB image and a cascade Render strategy. Given an RGB image as input, R2-Art estimates per-part 6D pose for the articulation. Specifically, we design parallel regression branches tailored to generate camera-to-root translation and rotation. Using the predicted joint states, we perform PC prior transformation and deformation with a joint-centric modeling approach. For further refinement, a cascade render strategy is proposed for projecting the 3D deformed prior onto the 2D mask. Extensive experiments are provided to validate our R2-Art on various datasets ranging from synthetic datasets to real-world scenarios, demonstrating the superior performance and robustness of the R2-Art. We believe that this work has the potential to be applied in many fields including robotics, embodied intelligence, and augmented reality.}, number={9}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhang, Li and Jiang, Haonan and Huo, Yukang and Zhong, Yan and Wang, Jianan and Wang, Xue and Wang, Rujing and Liu, Liu}, year={2025}, month={Apr.}, pages={9985-9993} }