Articulated objects are common in our daily life. However, current category-level articulation pose works mostly focus on predicting 9D poses on statistical point cloud observations. In this paper, we deal with the problem of category-level online robust 9D pose tracking of articulated objects, where we propose VoCAPTER, a novel 3D Voting-based Category-level Articulated object Pose TrackER. Our VoCAPTER efficiently updates poses between adjacent frames by utilizing partial observations from the current frame and the estimated per-part 9D poses from the previous frame. Specifically, by incorporating prior knowledge of continuous motion relationships between frames, we begin by canonicalizing the input point cloud, casting the pose tracking task as an inter-frame pose increment estimation challenge. Subsequently, to obtain a robust pose-tracking algorithm, our main idea is to leverage SE(3)-invariant features during motion. This is achieved through a voting-based articulation tracking algorithm, which identifies keyframes as reference states for accurate pose updating throughout the entire video sequence. We evaluate the performance of VoCAPTER in the synthetic dataset and real-world scenarios, which demonstrates VoCAPTER's generalization ability to diverse and complicated scenes. Through these experiments, we provide evidence of VoCAPTER's superiority and robustness in multi-frame pose tracking of articulated objects. We believe that this work can facilitate the progress of various fields, including robotics, embodied intelligence, and augmented reality. All the codes will be made publicly available.

Image Forgery Detection and Localization is rapidly advancing in the field of computer vision. Most methods locate forged regions in the form of segmentation and subsequently perform detection, facing challenges such as false detections (i.e., FPs) and inaccurate boundaries. In this work, we suggest rethinking the Image Forgery Detection and Localization (IFDL) task from a regression perspective and propose the CatmullRom Splines-based Regression Network (CSR-Net) to address these issues. Specifically, we first design an adaptive CutmullRom splines fitting scheme to predict coarse forged regions. Subsequently, we develop a novel rescoring mechanism that filters out samples with no response in both the classification and instance branches to reduce false positives. Besides, a learnable texture extraction module decouples horizontal and vertical forgery features, extracting more robust contour representations to further refine boundaries and suppress false detections. Compared to segmentation-based methods our method is simple but effective due to the unnecessity of post-processing. Extensive experiments conducted on several challenging benchmarks demonstrate that our method outperforms state-of-the-art methods qualitatively and quantitatively. Particularly, CSR-Net achieves optimal performance on three real-world datasets, indicating the applicability of our method to real scenarios such as social media and multi-tampered regions.