Joint-Motion Mutual Learning for Pose Estimation in Videos

• URL: http://arxiv.org/abs/2408.02285v1
• Date: Mon, 5 Aug 2024 07:37:55 GMT
• Title: Joint-Motion Mutual Learning for Pose Estimation in Videos
• Authors: Sifan Wu, Haipeng Chen, Yifang Yin, Sihao Hu, Runyang Feng, Yingying Jiao, Ziqi Yang, Zhenguang Liu,
• Abstract summary: Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. We propose a novel joint-motion mutual learning framework for pose estimation.
• Score: 21.77871402339573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a by-product of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatio-temporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks.

Related papers

• KinMo: Kinematic-aware Human Motion Understanding and Generation [6.962697597686156]
  Controlling human motion based on text presents an important challenge in computer vision. Traditional approaches often rely on holistic action descriptions for motion synthesis. We propose a novel motion representation that decomposes motion into distinct body joint group movements.
  arXiv  Detail & Related papers  (2024-11-23T06:50:11Z)
• Joint-Dataset Learning and Cross-Consistent Regularization for Text-to-Motion Retrieval [4.454835029368504]
  We focus on the recently introduced text-motion retrieval which aim to search for sequences that are most relevant to a natural motion description. Despite recent efforts to explore these promising avenues, a primary challenge remains the insufficient data available to train robust text-motion models. We propose to investigate joint-dataset learning - where we train on multiple text-motion datasets simultaneously. We also introduce a transformer-based motion encoder, called MoT++, which employs the specified-temporal attention to process sequences of skeleton data.
  arXiv  Detail & Related papers  (2024-07-02T09:43:47Z)
• Improving Video Violence Recognition with Human Interaction Learning on 3D Skeleton Point Clouds [88.87985219999764]
  We develop a method for video violence recognition from a new perspective of skeleton points. We first formulate 3D skeleton point clouds from human sequences extracted from videos. We then perform interaction learning on these 3D skeleton point clouds.
  arXiv  Detail & Related papers  (2023-08-26T12:55:18Z)
• Spatio-Temporal Branching for Motion Prediction using Motion Increments [55.68088298632865]
  Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications. Traditional methods rely on hand-crafted features and machine learning techniques. We propose a noveltemporal-temporal branching network using incremental information for HMP.
  arXiv  Detail & Related papers  (2023-08-02T12:04:28Z)
• Mutual Information-Based Temporal Difference Learning for Human Pose Estimation in Video [16.32910684198013]
  We present a novel multi-frame human pose estimation framework, which employs temporal differences across frames to model dynamic contexts. To be specific, we design a multi-stage entangled learning sequences conditioned on multi-stage differences to derive informative motion representation sequences. These place us to rank No.1 in the Crowd Pose Estimation in Complex Events Challenge on benchmark HiEve.
  arXiv  Detail & Related papers  (2023-03-15T09:29:03Z)
• Motion Prediction via Joint Dependency Modeling in Phase Space [40.54430409142653]
  We introduce a novel convolutional neural model to leverage explicit prior knowledge of motion anatomy. We then propose a global optimization module that learns the implicit relationships between individual joint features. Our method is evaluated on large-scale 3D human motion benchmark datasets.
  arXiv  Detail & Related papers  (2022-01-07T08:30:01Z)
• Video Salient Object Detection via Contrastive Features and Attention Modules [106.33219760012048]
  We propose a network with attention modules to learn contrastive features for video salient object detection. A co-attention formulation is utilized to combine the low-level and high-level features. We show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-03T17:40:32Z)
• Event-based Motion Segmentation with Spatio-Temporal Graph Cuts [51.17064599766138]
  We have developed a method to identify independently objects acquired with an event-based camera. The method performs on par or better than the state of the art without having to predetermine the number of expected moving objects.
  arXiv  Detail & Related papers  (2020-12-16T04:06:02Z)
• Pose And Joint-Aware Action Recognition [87.4780883700755]
  We present a new model for joint-based action recognition, which first extracts motion features from each joint separately through a shared motion encoder. Our joint selector module re-weights the joint information to select the most discriminative joints for the task. We show large improvements over the current state-of-the-art joint-based approaches on JHMDB, HMDB, Charades, AVA action recognition datasets.
  arXiv  Detail & Related papers  (2020-10-16T04:43:34Z)
• Hierarchical Contrastive Motion Learning for Video Action Recognition [100.9807616796383]
  We present hierarchical contrastive motion learning, a new self-supervised learning framework to extract effective motion representations from raw video frames. Our approach progressively learns a hierarchy of motion features that correspond to different abstraction levels in a network. Our motion learning module is lightweight and flexible to be embedded into various backbone networks.
  arXiv  Detail & Related papers  (2020-07-20T17:59:22Z)

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.