Following the success of deep neural networks in 2D pose estimation, reconstruction-based approaches have significantly advanced multi-person 3D pose estimation from sparse multi-view images. These methods typically detect 2D poses independently in each view and then associate them for 3D reconstruction. However, despite strong progress, recent state-of-the-art methods still face critical limitations: 1) They often depend on global optimization over a large and complex set of multi-view 2D joints to jointly infer 3D poses for all individuals, making the process highly complex and prone to suboptimal solutions; 2) Their tight coupling with the bottom-up detector OpenPose hinders the use of more advanced top-down or single-stage 2D pose estimators and restricts the integration of richer instance-level cues learned by these models. To address these limitations, we propose TwinPose, a novel framework that alleviates the complexity of global pose inference by optimizing within person-specific 3D pose subspaces, while fully supporting diverse 2D pose detectors and effectively leveraging pose-instance cues. The key idea is to introduce a twin pose — a 3D counterpart of each 2D pose — that inherits its instance representation and aggregates geometrically consistent 2D joints from other views. All twin poses are unified in a common 3D space, where those belonging to the same individual naturally share a number of bones. This structural property enables association by counting shared bones, forming person-specific subspaces from which each individual's 3D pose can be inferred independently in an efficient and robust manner. Extensive experiments demonstrate that TwinPose achieves state-of-the-art performance in both accuracy and efficiency across multiple public and proprietary datasets. Importantly, it is fully detector-agnostic, allowing seamless integration with current and future advances in 2D pose estimation while remaining highly robust to noisy or imperfect 2D predictions. Project page with code and additional resources: https://github.com/zgspose/TwinPose

This article focuses on the secure predefined-time sliding mode control problem for a third-order heterogeneous vehicle platoon. For the purpose of reducing the effect due to the sensor measurement deviation and relaxing the system conservatism, a state estimation algorithm is designed based on the historical data and threshold judgment. Furthermore, a rate-coded reversible privacy-preserving mechanism with dual encryption is proposed and applied to vehicle-to-vehicle communications, which can guarantee the protection of the critical system data and the realizability of the desired predefined-time convergence performance by utilizing the origin outputs. In order to avoid the singularity problem and enhance the resistance of the vehicle platoon to the external disturbance, a nonsingular sliding mode surface and a corresponding predefined-time controller are designed. Based on the predefined-time stability and the string stability theorems, the vehicle platoon can be proved to be a practical predefined-time stable (PPTS) and string stable. Finally, adequate validations of the third-order heterogeneous vehicle platoon demonstrate the fast convergence speed and good robustness of the proposed control scheme.

The proliferation of Internet of Things (IoT) devices with weak passwords poses significant challenges to network security. Hackers can easily compromise these IoT devices by brute-forcing their passwords and then utilize them to launch severe attacks such as Distributed Denial of Service (DDoS). To defeat such threats, an effective solution is to ensure that each device has a strong, high-strength password. However, in practice, this poses a significant challenge, as configuring and managing passwords for such a vast number of devices is highly complex. In this work, we propose a novel Multi-Device Collaborative Authentication (MDCA) system that enables IoT devices to be protected by strong passwords without altering their original weak ones. Our approach is inspired by group defense strategies prevalent in nature: animals cooperate and form groups to enhance their chances of survival. Intuitively, IoT devices can also collaborate to improve their security. Specifically, in our design, devices with strong passwords provide authentication for devices having weak credentials, and the latter can generate clues to detect brute-force attacks. Once these attacks are detected, requests to IoT devices are first authenticated by several other strong password protected devices, and if all authentications succeed, the requests are then permitted. We have implemented the proposed system and theoretically analyzed its security. The experimental results match the theoretical analysis well.