Skeletons Speak Louder than Text: A Motion-Aware Pretraining Paradigm for Video-Based Person Re-Identification

• URL: http://arxiv.org/abs/2511.13150v1
• Date: Mon, 17 Nov 2025 08:59:41 GMT
• Title: Skeletons Speak Louder than Text: A Motion-Aware Pretraining Paradigm for Video-Based Person Re-Identification
• Authors: Rifen Lin, Alex Jinpeng Wang, Jiawei Mo, Min Li,
• Abstract summary: Multimodal pretraining has revolutionized visual understanding, but its impact on person-based person re-identification (ReID) remains underexplored.<n>Existing approaches often rely on video-text pairs, yet suffer from two fundamental limitations: (1) lack of genuine multimodal pretraining, and (2) text poorly captures fine-grained temporal motion.<n>We take a bold departure from text-based paradigms by introducing the first skeleton-driven pretraining framework for ReID.
• Score: 8.135364788458423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal pretraining has revolutionized visual understanding, but its impact on video-based person re-identification (ReID) remains underexplored. Existing approaches often rely on video-text pairs, yet suffer from two fundamental limitations: (1) lack of genuine multimodal pretraining, and (2) text poorly captures fine-grained temporal motion-an essential cue for distinguishing identities in video. In this work, we take a bold departure from text-based paradigms by introducing the first skeleton-driven pretraining framework for ReID. To achieve this, we propose Contrastive Skeleton-Image Pretraining for ReID (CSIP-ReID), a novel two-stage method that leverages skeleton sequences as a spatiotemporally informative modality aligned with video frames. In the first stage, we employ contrastive learning to align skeleton and visual features at sequence level. In the second stage, we introduce a dynamic Prototype Fusion Updater (PFU) to refine multimodal identity prototypes, fusing motion and appearance cues. Moreover, we propose a Skeleton Guided Temporal Modeling (SGTM) module that distills temporal cues from skeleton data and integrates them into visual features. Extensive experiments demonstrate that CSIP-ReID achieves new state-of-the-art results on standard video ReID benchmarks (MARS, LS-VID, iLIDS-VID). Moreover, it exhibits strong generalization to skeleton-only ReID tasks (BIWI, IAS), significantly outperforming previous methods. CSIP-ReID pioneers an annotation-free and motion-aware pretraining paradigm for ReID, opening a new frontier in multimodal representation learning.

Related papers

• X-ReID: Multi-granularity Information Interaction for Video-Based Visible-Infrared Person Re-Identification [79.37768038337971]
  We propose a novel cross-modality feature learning framework named X-ReID for VVI-ReID.<n> Specifically, we first propose a Cross-modality Prototype Collaboration (CPC)<n>Then, a Multi-granularity Information Interaction (MII) is designed, incorporating short-term interactions from adjacent frames, long-term cross-frame information fusion, and cross-modality feature alignment.
  arXiv  Detail & Related papers  (2025-11-22T07:57:15Z)
• ViSS-R1: Self-Supervised Reinforcement Video Reasoning [84.1180294023835]
  We introduce a novel self-supervised reinforcement learning GRPO algorithm (Pretext-GRPO) within the standard R1 pipeline.<n>We also propose the ViSS-R1 framework, which streamlines and integrates pretext-task-based self-supervised learning directly into the MLLM's R1 post-training paradigm.
  arXiv  Detail & Related papers  (2025-11-17T07:00:42Z)
• DINOv2 Driven Gait Representation Learning for Video-Based Visible-Infrared Person Re-identification [30.593882551803855]
  Video-based Visible-Infrared person re-identification (VVI-ID) aims to retrieve the same pedestrian across visible and infrared modalities from modality.<n>To address these challenges, we propose a Gait Representation Learning framework that leverages the rich visual priors of DINOv2 to learn gait features complementary to appearance cues.<n>Specifically, we introduce a Semantic-Aware Silhouette and Gait Learning (GL) model, which generates and enhances silhouette representations with general-purpose semantic priors from DINOv2.
  arXiv  Detail & Related papers  (2025-11-06T11:21:13Z)
• FrameMind: Frame-Interleaved Video Reasoning via Reinforcement Learning [65.42201665046505]
  Current video understanding models rely on fixed frame sampling strategies, processing predetermined visual inputs regardless of the specific reasoning requirements of each question.<n>This static approach limits their ability to adaptively gather visual evidence, leading to suboptimal performance on tasks that require broad temporal coverage or fine-grained spatial detail.<n>We introduce FrameMind, an end-to-end framework trained with reinforcement learning that enables models to dynamically request visual information during reasoning through Frame-Interleaved Chain-of-Thought (FiCOT)<n>Unlike traditional approaches, FrameMind operates in multiple turns where the model alternates between textual reasoning and active visual perception, using tools to extract
  arXiv  Detail & Related papers  (2025-09-28T17:59:43Z)
• When the Future Becomes the Past: Taming Temporal Correspondence for Self-supervised Video Representation Learning [80.09819072780193]
  We propose a self-supervised framework that leverages Temporal Correspondence for video representation learning (T-CoRe)<n>Experiments of T-CoRe consistently present superior performance across several downstream tasks, demonstrating its effectiveness for video representation learning.
  arXiv  Detail & Related papers  (2025-03-19T10:50:03Z)
• Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning [20.34477942813382]
  Skeleton-based action representation learning aims to interpret and understand human behaviors by encoding the skeleton sequences. We introduce a novel skeleton-based training framework based on Cross-modal Contrastive learning. Our method outperforms the previous methods and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2024-05-31T03:40:15Z)
• Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder [58.523884148942166]
  We propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework. First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin to establish good frame-level syllable boundaries from lip shapes. Next, we propose an audio-guided cross-modal fusion encoder (CMFE) neural network to utilize main training parameters for multiple cross-modal attention layers.
  arXiv  Detail & Related papers  (2023-08-14T08:19:24Z)
• DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation [37.25815760042241]
  This paper introduces a new framework, dubbed DirecT2V, to generate text-to-video (T2V) videos. We equip a diffusion model with a novel value mapping method and dual-softmax filtering, which do not require any additional training. The experimental results validate the effectiveness of our framework in producing visually coherent and storyful videos.
  arXiv  Detail & Related papers  (2023-05-23T17:57:09Z)
• Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring [82.84513669453744]
  Image-text pretrained models, e.g., CLIP, have shown impressive general multi-modal knowledge learned from large-scale image-text data pairs. We revisit temporal modeling in the context of image-to-video knowledge transferring. We present a simple and effective temporal modeling mechanism extending CLIP model to diverse video tasks.
  arXiv  Detail & Related papers  (2023-01-26T14:12:02Z)
• SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identification [63.903237777588316]
  We present a generic Simple Masked Contrastive learning (SimMC) framework to learn effective representations from unlabeled 3D skeletons for person re-ID. Specifically, to fully exploit skeleton features within each skeleton sequence, we first devise a masked prototype contrastive learning (MPC) scheme. Then, we propose the masked intra-sequence contrastive learning (MIC) to capture intra-sequence pattern consistency between subsequences.
  arXiv  Detail & Related papers  (2022-04-21T00:19:38Z)

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.