Topology-Aware Skeleton Detection via Lighthouse-Guided Structured Inference

• URL: http://arxiv.org/abs/2604.20123v1
• Date: Wed, 22 Apr 2026 02:43:10 GMT
• Title: Topology-Aware Skeleton Detection via Lighthouse-Guided Structured Inference
• Abstract summary: Even slight variations in pose or movement can cause noticeable changes in skeleton structure.<n>Existing methods primarily focus on point-level skeleton point detection.<n>We propose Lighthouse-Skel, a topology-aware skeleton detection method via lighthouse-guided structured inference.
• Score: 8.261311489857757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In natural images, object skeletons are used to represent geometric shapes. However, even slight variations in pose or movement can cause noticeable changes in skeleton structure, increasing the difficulty of detecting the skeleton and often resulting in discontinuous skeletons. Existing methods primarily focus on point-level skeleton point detection and overlook the importance of structural continuity in recovering complete skeletons. To address this issue, we propose Lighthouse-Skel, a topology-aware skeleton detection method via lighthouse-guided structured inference. Specifically, we introduce a dual-branch collaborative detection framework that jointly learns skeleton confidence field and structural anchors, including endpoints and junction points. The spatial distributions learned by the point branch guide the network to focus on topologically vulnerable regions, which improves the accuracy of skeleton detection. Based on the learned skeleton confidence field, we further propose a lighthouse-guided topology completion strategy, which uses detected junction points and breakpoints as lighthouses to reconnect discontinuous skeleton segments along low-cost paths, thereby improving skeleton continuity and structural integrity. Experimental results on four public datasets demonstrate that the proposed method achieves competitive detection accuracy while substantially improving skeleton connectivity and structural integrity.

Related papers

• PALUM: Part-based Attention Learning for Unified Motion Retargeting [53.17113525688095]
  Remotion between characters with different skeleton structures is a fundamental challenge in computer animation.<n>We present a novel approach that learns common motion representations across diverse skeleton topologies.<n>Experiments demonstrate superior performance in handling diverse skeletal structures while maintaining motion realism and semantic fidelity.
  arXiv  Detail & Related papers  (2026-01-12T07:29:44Z)
• Motif Guided Graph Transformer with Combinatorial Skeleton Prototype Learning for Skeleton-Based Person Re-Identification [60.939250172443586]
  Person re-identification (re-ID) via 3D skeleton data is a challenging task with significant value in many scenarios.<n>Existing skeleton-based methods typically assume virtual motion relations between all joints, and adopt average joint or sequence representations for learning.<n>This paper presents a generic Motif guided graph transformer with Combinatorial skeleton prototype learning (MoCos)<n>MoCos exploits structure-specific and gait-related body relations as well as features of skeleton graphs to learn effective skeleton representations for person re-ID.
  arXiv  Detail & Related papers  (2024-12-12T08:13:29Z)
• Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition [64.56321246196859]
  We propose a novel dyNamically Evolving dUal skeleton-semantic syneRgistic framework.<n>We first construct the spatial-temporal evolving micro-prototypes and integrate dynamic context-aware side information.<n>We introduce the spatial compression and temporal memory mechanisms to guide the growth of spatial-temporal micro-prototypes.
  arXiv  Detail & Related papers  (2024-11-18T05:16:11Z)
• GaitSTR: Gait Recognition with Sequential Two-stream Refinement [12.256802601846749]
  Gait recognition aims to identify a person based on their walking sequences, serving as a useful biometric modality. In representing a person's walking sequence, silhouettes and skeletons are the two primary modalities used. We explore the use of a two-stream representation of skeletons for gait recognition, alongside silhouettes.
  arXiv  Detail & Related papers  (2024-04-02T22:39:35Z)
• SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration [20.32815928095936]
  Point cloud registration has remained largely unexplored in cross-source point clouds and unstructured scenes. We propose a novel method termed SPEAL to leverage skeletal representations for effective learning of intrinsic topologies of point clouds. To the best of our knowledge, our approach is the first to facilitate point cloud registration with skeletal geometric priors.
  arXiv  Detail & Related papers  (2023-12-14T05:35:51Z)
• SkeletonGait: Gait Recognition Using Skeleton Maps [7.335859292188816]
  We introduce a novel skeletal gait representation named skeleton map, together with SkeletonGait, a skeleton-based method to exploit structural information from human skeleton maps. Skeleton map represents the coordinates of human joints as a heatmap with Gaussian approximation, exhibiting a silhouette-like image devoid of exact body structure. SkeletonGait++ outperforms existing state-of-the-art methods by a significant margin in various scenarios.
  arXiv  Detail & Related papers  (2023-11-22T15:09:59Z)
• Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-Identification [70.90142717649785]
  This paper proposes a generic unsupervised Hierarchical skeleton Meta-Prototype Contrastive learning (Hi-MPC) approach with Hard Skeleton Mining (HSM) for person re-ID with unlabeled 3D skeletons. By converting original prototypes into meta-prototypes with multiple homogeneous transformations, we induce the model to learn the inherent consistency of prototypes to capture more effective skeleton features for person re-ID.
  arXiv  Detail & Related papers  (2023-07-24T16:18:22Z)
• Point2Skeleton: Learning Skeletal Representations from Point Clouds [36.62519847312199]
  We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. We first predict a set of skeletal points by learning a geometric transformation, and then analyze the connectivity of the skeletal points to form skeletal mesh structures.
  arXiv  Detail & Related papers  (2020-12-01T03:04:09Z)
• SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images [85.66560542483286]
  This paper focuses on the challenging task of learning 3D object surface reconstructions from RGB images. We propose two models, the Skeleton-Based GraphConvolutional Neural Network (SkeGCNN) and the Skeleton-Regularized Deep Implicit Surface Network (SkeDISN) We conduct thorough experiments that verify the efficacy of our proposed SkeletonNet.
  arXiv  Detail & Related papers  (2020-08-13T07:59:25Z)
• Skeleton-Aware Networks for Deep Motion Retargeting [83.65593033474384]
  We introduce a novel deep learning framework for data-driven motion between skeletons. Our approach learns how to retarget without requiring any explicit pairing between the motions in the training set.
  arXiv  Detail & Related papers  (2020-05-12T12:51:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.