Combo-Gait: Unified Transformer Framework for Multi-Modal Gait Recognition and Attribute Analysis

• URL: http://arxiv.org/abs/2510.10417v1
• Date: Sun, 12 Oct 2025 02:56:40 GMT
• Title: Combo-Gait: Unified Transformer Framework for Multi-Modal Gait Recognition and Attribute Analysis
• Authors: Zhao-Yang Wang, Zhimin Shao, Jieneng Chen, Rama Chellappa,
• Abstract summary: We propose a framework that combines 2D temporal silhouettes with 3D SMPL features for robust gait analysis.<n>Beyond identification, we introduce a multitask learning strategy that jointly performs gait recognition and human attribute estimation.<n>Our approach outperforms state-of-the-art methods in gait recognition and provides accurate human attribute estimation.
• Score: 35.9240903956677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gait recognition is an important biometric for human identification at a distance, particularly under low-resolution or unconstrained environments. Current works typically focus on either 2D representations (e.g., silhouettes and skeletons) or 3D representations (e.g., meshes and SMPLs), but relying on a single modality often fails to capture the full geometric and dynamic complexity of human walking patterns. In this paper, we propose a multi-modal and multi-task framework that combines 2D temporal silhouettes with 3D SMPL features for robust gait analysis. Beyond identification, we introduce a multitask learning strategy that jointly performs gait recognition and human attribute estimation, including age, body mass index (BMI), and gender. A unified transformer is employed to effectively fuse multi-modal gait features and better learn attribute-related representations, while preserving discriminative identity cues. Extensive experiments on the large-scale BRIAR datasets, collected under challenging conditions such as long-range distances (up to 1 km) and extreme pitch angles (up to 50{\deg}), demonstrate that our approach outperforms state-of-the-art methods in gait recognition and provides accurate human attribute estimation. These results highlight the promise of multi-modal and multitask learning for advancing gait-based human understanding in real-world scenarios.

Related papers

• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
  This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
  arXiv  Detail & Related papers  (2026-01-13T23:36:30Z)
• RingMo-Agent: A Unified Remote Sensing Foundation Model for Multi-Platform and Multi-Modal Reasoning [15.670921552151775]
  RingMo-Agent is designed to handle multi-modal and multi-platform data.<n>It is supported by a large-scale vision-language dataset named RS-VL3M.<n>It proves effective in both visual understanding and sophisticated analytical tasks.
  arXiv  Detail & Related papers  (2025-07-28T12:39:33Z)
• Multilinear subspace learning for person re-identification based fusion of high order tensor features [2.03240755905453]
  PRe-ID aims to identify and track target individuals who have already been detected in a network of cameras.<n>To this end, two powerful features, Conal Neural Networks (CNN) and Local Maximal Occurrence (LOMO) are modeled on multidimensional data.<n>New tensor fusion scheme is introduced to leverage and combine these two types of features in a single tensor.
  arXiv  Detail & Related papers  (2025-05-09T23:39:27Z)
• SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection [73.49799596304418]
  This paper introduces a new task called Multi-Modal datasets and Multi-Task Object Detection (M2Det) for remote sensing.<n>It is designed to accurately detect horizontal or oriented objects from any sensor modality.<n>This task poses challenges due to 1) the trade-offs involved in managing multi-modal modelling and 2) the complexities of multi-task optimization.
  arXiv  Detail & Related papers  (2024-12-30T02:47:51Z)
• GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning [51.677086019209554]
  We propose a Generalized Structural Sparse to capture powerful relationships across modalities for pair-wise similarity learning. The distance metric delicately encapsulates two formats of diagonal and block-diagonal terms. Experiments on cross-modal and two extra uni-modal retrieval tasks have validated its superiority and flexibility.
  arXiv  Detail & Related papers  (2024-10-20T03:45:50Z)
• GaitMA: Pose-guided Multi-modal Feature Fusion for Gait Recognition [26.721242606715354]
  Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. We propose a novel gait recognition framework, dubbed Gait Multi-model Aggregation Network (GaitMA) First, skeletons are represented by joint/limb-based heatmaps, and features from silhouettes and skeletons are respectively extracted using two CNN-based feature extractors.
  arXiv  Detail & Related papers  (2024-07-20T09:05:17Z)
• MuJo: Multimodal Joint Feature Space Learning for Human Activity Recognition [2.7532797256542403]
  Human activity recognition (HAR) is a long-standing problem in artificial intelligence with applications in a broad range of areas.<n>We introduce our comprehensive Fitness Multimodal Activity dataset (FiMAD) to enhance HAR performance across various modalities.<n>We show that FiMAD can increase the performance on real HAR datasets such as MM-Fit, MyoGym, MotionSense, and MHEALTH.
  arXiv  Detail & Related papers  (2024-06-06T08:42:36Z)
• Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition [6.995226697189459]
  We employ a multi-modal self-supervised learning method for facial expression recognition from in-the-wild video data. Our results generally show that multi-modal self-supervision tasks offer large performance gains for challenging tasks. We release our pre-trained models as well as source code publicly.
  arXiv  Detail & Related papers  (2024-04-16T20:51:36Z)
• Towards Unified 3D Object Detection via Algorithm and Data Unification [70.27631528933482]
  We build the first unified multi-modal 3D object detection benchmark MM- Omni3D and extend the aforementioned monocular detector to its multi-modal version. We name the designed monocular and multi-modal detectors as UniMODE and MM-UniMODE, respectively.
  arXiv  Detail & Related papers  (2024-02-28T18:59:31Z)
• UniM$^2$AE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving [47.590099762244535]
  Masked Autoencoders (MAE) play a pivotal role in learning potent representations, delivering outstanding results across various 3D perception tasks. This research delves into multi-modal Masked Autoencoders tailored for a unified representation space in autonomous driving. To intricately marry the semantics inherent in images with the geometric intricacies of LiDAR point clouds, we propose UniM$2$AE.
  arXiv  Detail & Related papers  (2023-08-21T02:13:40Z)
• Gait Recognition in the Wild with Multi-hop Temporal Switch [81.35245014397759]
  gait recognition in the wild is a more practical problem that has attracted the attention of the community of multimedia and computer vision. This paper presents a novel multi-hop temporal switch method to achieve effective temporal modeling of gait patterns in real-world scenes.
  arXiv  Detail & Related papers  (2022-09-01T10:46:09Z)
• Adversarial Multi-scale Feature Learning for Person Re-identification [0.0]
  Person ReID aims to accurately measure visual similarities between person images for determining whether two images correspond to the same person. We propose to improve Person ReID system performance from two perspective: textbf1). Multi-scale feature learning (MSFL), which consists of Cross-scale information propagation (CSIP) and Multi-scale feature fusion (MSFF), to dynamically fuse features cross different scales. Multi-scale gradient regularizor (MSGR), to emphasize ID-related factors and ignore irrelevant factors in an adversarial manner.
  arXiv  Detail & Related papers  (2020-12-28T02:18:00Z)
• Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition [89.0152015268929]
  We propose the first neural architecture search (NAS)-based method for RGB-D gesture recognition. The proposed method includes two key components: 1) enhanced temporal representation via the 3D Central Difference Convolution (3D-CDC) family, and optimized backbones for multi-modal-rate branches and lateral connections. The resultant multi-rate network provides a new perspective to understand the relationship between RGB and depth modalities and their temporal dynamics.
  arXiv  Detail & Related papers  (2020-08-21T10:45:09Z)

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.