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Parse Graph-Based Visual-Language Interaction for Human Pose Estimation

arXiv 2509.07385 ↗First posted 9 Sep 2025cs.CVcited by 0

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Abstract (arXiv)

Parse graphs boost human pose estimation (HPE) by integrating context and hierarchies, yet prior work mostly focuses on single modality modeling, ignoring the potential of multimodal fusion. Notably, language offers rich HPE priors like spatial relations for occluded scenes, but existing visual-language fusion via global feature integration weakens occluded region responses and causes alignment and location failures. To address this issue, we propose Parse Graph-based Visual-Language interaction (PGVL) with a core novel Guided Module (GM). In PGVL, low-level nodes focus on local features, maximizing the maintenance of responses in occluded areas and high-level nodes integrate global features to infer occluded or invisible parts. GM enables high semantic nodes to guide the feature update of low semantic nodes that have undergone cross attention. It ensuring effective fusion of diverse information. PGVL includes top-down decomposition and bottom-up composition. In the first stage, modality specific parse graphs are constructed. Next stage. recursive bidirectional cross-attention is used, purified by GM. We also design network based on PGVL. The PGVL and our network is validated on major pose estimation datasets. We will release the code soon.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 49 references that are in the map. First posted on arXiv 9 Sep 2025.

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32 of 49 references.

References · 32

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20 more
  1. 2022ViTPose: Simple Vision Transformer Baselines for Human Pose Estimationcited by 456
  2. 2018CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmarkcited by 214
  3. 2019MirrorGAN: Learning Text-to-image Generation by Redescriptioncited by 176
  4. 2019Distribution-Aware Coordinate Representation for Human Pose Estimationcited by 165
  5. 2021HRFormer: High-Resolution Transformer for Dense Predictioncited by 149
  6. 2021AP-10K: A Benchmark for Animal Pose Estimation in the Wildcited by 123
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  8. 2019Cross-Domain Adaptation for Animal Pose Estimationcited by 108
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  12. 2023RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimationcited by 34
  13. 2021Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimationcited by 30
  14. 2022CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Posecited by 25
  15. 2022Referring Image Mattingcited by 22
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  19. 2025Learning Structure-Supporting Dependencies via Keypoint Interactive Transformer for General Mammal Pose Estimationcited by 3
  20. 2025Probabilistic Prompt Distribution Learning for Animal Pose Estimationcited by 3

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