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

Jiwei Ding, Wei Hu, Qixin Xu, Yuzhong Qu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

The quantity of entities in the Linked Data is increasing rapidly. For entity search and browsing systems, filtering is very useful for users to find entities that they are interested in. Type is a kind of widely-used facet and can be easily obtained from knowledge bases, which enables to create filters by selecting at most K types of an entity collection. However, existing approaches often fail to select high-quality type filters due to complex overlap between types. In this paper, we propose a novel type selection approach based upon Budgeted Maximum Coverage (BMC), which can achieve integral optimization for the coverage quality of type filters. Furthermore, we define a new optimization problem called Extended Budgeted Maximum Coverage (EBMC) and propose an EBMC-based approach, which enhances the BMC-based approach by incorporating the relevance between entities and types, so as to create sensible type filters. Our experimental results show that the EBMC-based approach performs best comparing with several representative approaches.