Simple Baseline has become a dominant benchmark in human pose estimation (HPE) due to its excellent performance and simple design. However, its “strong encoder + simple decoder” architectural paradigm suffers from two core limitations: (1) its non-branching, linear deconvolutional path prevents it from leveraging the rich, fine-grained features generated by the encoder at multiple scales and (2) the model lacks explicit prior knowledge of both the absolute positions and structural layout of human keypoints. To address these issues, this paper introduces AFJ-PoseNet, a new architecture that deeply enhances the Simple Baseline framework. First, we restructure Simple Baseline’s original linear decoder into a U-Net-like multi-scale fusion path, introducing intermediate features from the encoder via skip connections. For efficient fusion, we design a novel Attention Fusion Module (AFM), which dynamically gates the flow of incoming detailed features through a context-aware spatial attention mechanism. Second, we propose the Joint-Aware Positional Encoding (JAPE) module, which innovatively combines a fixed global coordinate system with learnable, joint-specific spatial priors. This design injects both absolute position awareness and statistical priors of the human body structure. Our ablation studies on the MPII dataset validate the effectiveness of each proposed enhancement, with our full model achieving a mean PCKh of 88.915, a 0.341 percentage point improvement over our re-implemented baseline. On the more challenging COCO val2017 dataset, our ResNet-50-based AFJ-PoseNet achieves an Average Precision (AP) of 72.6%. While this involves a slight trade-off in Average Recall for higher precision, this result represents a significant 2.2 percentage point improvement over our re-implemented baseline (70.4%) and also outperforms other strong, publicly available models like DARK (72.4%) and SimCC (72.1%) under comparable settings, demonstrating the superiority and competitiveness of our proposed enhancements.

To address the problems of blurred lesion boundaries, noise interference, and the lack of lightweight design in segmentation models for breast ultrasound images, this paper proposes a lightweight, high-real-time segmentation model, MV-UNet, based on Mamba architecture. The model employs an improved MambaVision encoder paired with a UNetMamba decoder. This architecture, augmented by a Local Supervision Module (LSM) during training, effectively integrates global context with local details while maintaining linear computational complexity, thereby enhancing boundary delineation capability. The experimental results on the BUSI_WHU dataset show that the MV-UNet achieves 90.51% in mIoU, 90.85% in Recall, and 4.59 pixels in ASSD, surpassing most of the existing advanced models in multiple metrics. At the same time, the number of parameters is only 14.7% of the EMGANet, and the inference speed is increased by 3.2 times. Furthermore, an independent benchmark test on the BUSI dataset demonstrates the model’s practical efficiency, achieving an ASSD of 14.94 pixels while maintaining its clear advantages in model lightness and inference speed. In summary, the excellent balance between segmentation accuracy and model efficiency achieved by MV-UNet provides a novel and effective approach for breast ultrasound image segmentation.

The growing demand for real-time human motion reconstruction in Virtual Reality (VR), Augmented Reality (AR), and the Metaverse requires high accuracy with minimal hardware. This paper presents SSR-HMR, a skeleton-aware, sparse node-based method for full-body motion reconstruction from limited inputs. The approach incorporates a lightweight spatiotemporal graph convolutional module, a torso pose refinement design to mitigate orientation drift, and kinematic tree-based optimization to enhance end-effector positioning accuracy. Smooth motion transitions are achieved via a multi-scale velocity loss. Experiments demonstrate that SSR-HMR achieves high-accuracy reconstruction, with mean joint and end-effector position errors of 1.06 cm and 0.52 cm, respectively, while operating at 267 FPS on a CPU.

Accurate full-body motion reconstruction from sparse sensors is crucial for VR/AR applications but remains challenging due to the under-constrained nature of limited observations and the computational constraints of mobile platforms. This paper presents LGSIK-Poser, a unified and lightweight framework that supports real-time motion reconstruction from heterogeneous sensor configurations, including head-mounted displays, handheld controllers, and up to three optional inertial measurement units, without requiring reconfiguration across scenarios. The model integrates temporally grouped LSTM modeling, anatomically structured graph-based reasoning, and region-specific inverse kinematics refinement to enhance end-effector accuracy and structural consistency. Personalized body shape is estimated using user-specific anthropometric priors within the SMPL model, a widely adopted parametric representation of human shape and pose. Experiments on the AMASS benchmark demonstrate that LGSIK-Poser achieves state-of-the-art accuracy with up to 48% improvement in hand localization, while reducing model size by 60% and latency by 22% compared to HMD-Poser. The system runs at 63.65 FPS with only 3.74 M parameters, highlighting its suitability for real-time immersive applications.