Learning Geometric Invariance for Gait Recognition

• URL: http://arxiv.org/abs/2601.05604v1
• Date: Fri, 09 Jan 2026 07:47:01 GMT
• Title: Learning Geometric Invariance for Gait Recognition
• Abstract summary: We argue that variations in different gait conditions can be approximately viewed as a combination of geometric transformations.<n>We design a $mathcalR$eflect-$mathcalR$otate-$mathcalS$cale invariance learning framework, named $mathcalRRS$-Gait.<n>Experiments on four popular gait datasets show superior performance across various gait conditions.
• Score: 43.26239161303881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of gait recognition is to extract identity-invariant features of an individual under various gait conditions, e.g., cross-view and cross-clothing. Most gait models strive to implicitly learn the common traits across different gait conditions in a data-driven manner to pull different gait conditions closer for recognition. However, relatively few studies have explicitly explored the inherent relations between different gait conditions. For this purpose, we attempt to establish connections among different gait conditions and propose a new perspective to achieve gait recognition: variations in different gait conditions can be approximately viewed as a combination of geometric transformations. In this case, all we need is to determine the types of geometric transformations and achieve geometric invariance, then identity invariance naturally follows. As an initial attempt, we explore three common geometric transformations (i.e., Reflect, Rotate, and Scale) and design a $\mathcal{R}$eflect-$\mathcal{R}$otate-$\mathcal{S}$cale invariance learning framework, named ${\mathcal{RRS}}$-Gait. Specifically, it first flexibly adjusts the convolution kernel based on the specific geometric transformations to achieve approximate feature equivariance. Then these three equivariant-aware features are respectively fed into a global pooling operation for final invariance-aware learning. Extensive experiments on four popular gait datasets (Gait3D, GREW, CCPG, SUSTech1K) show superior performance across various gait conditions.

Related papers

• E3DGS: Unified Geometric-Photometric Equivariance for 3D Gaussian Splatting via Color-as-Geometry Embedding [9.35652906982887]
  3D Gaussian Splatting (3DGS) captures scenes by coupling explicit geometry with view-dependent photometry (Spherical Harmonics)<n>Color has been treated as a signal rather than a geometric entity, making it nontrivial to unify symmetry across geometry and appearance as the camera frame changes.<n>We present E3DGS, a rigid-body ($mathrmSE(3)$)variant architecture that processes 3D Gaussians without Clebsch-Gordan tensor products.
  arXiv  Detail & Related papers  (2026-07-17T01:12:26Z)
• From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting [8.895952132148304]
  We provide evidence that native primitive attributes alone carry sufficient signal for scene change detection.<n> operating directly on primitives gives our method, GD-DIFF, two properties that distinguish it from render-then-compare methods.
  arXiv  Detail & Related papers  (2026-05-08T03:50:10Z)
• GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping [3.9108453320793326]
  We propose GAGrasp, a novel framework for dexterous grasp generation.<n>By encoding the SE(3) symmetry constraint directly into the architecture, our method improves data and parameter efficiency.<n>We incorporate a differentiable physics-informed refinement layer, which ensures that generated grasps are physically plausible and stable.
  arXiv  Detail & Related papers  (2025-03-06T06:00:55Z)
• Disentangled Representation Learning with the Gromov-Monge Gap [65.73194652234848]
  Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning.<n>We introduce a novel approach to disentangled representation learning based on quadratic optimal transport.<n>We demonstrate the effectiveness of our approach for quantifying disentanglement across four standard benchmarks.
  arXiv  Detail & Related papers  (2024-07-10T16:51:32Z)
• MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformations [2.2954246824369218]
  We propose a novel model that generates augmenting transformations in a multimodal latent space of geometric deformations.<n> Experimental results show that our proposed approach outperforms all baselines by significantly improved prediction accuracy.
  arXiv  Detail & Related papers  (2023-12-20T21:30:55Z)
• Self-Supervised Learning from Non-Object Centric Images with a Geometric Transformation Sensitive Architecture [7.825153552141346]
  We propose a Geometric Transformation Sensitive Architecture to be sensitive to geometric transformations. Our method encourages the student to be sensitive by predicting rotation and using targets that vary with those transformations. Our approach demonstrates improved performance when using non-object-centric images as pretraining data.
  arXiv  Detail & Related papers  (2023-04-17T06:32:37Z)
• Equivalence Between SE(3) Equivariant Networks via Steerable Kernels and Group Convolution [90.67482899242093]
  A wide range of techniques have been proposed in recent years for designing neural networks for 3D data that are equivariant under rotation and translation of the input. We provide an in-depth analysis of both methods and their equivalence and relate the two constructions to multiview convolutional networks. We also derive new TFN non-linearities from our equivalence principle and test them on practical benchmark datasets.
  arXiv  Detail & Related papers  (2022-11-29T03:42:11Z)
• 3D Equivariant Graph Implicit Functions [51.5559264447605]
  We introduce a novel family of graph implicit functions with equivariant layers that facilitates modeling fine local details. Our method improves over the existing rotation-equivariant implicit function from 0.69 to 0.89 on the ShapeNet reconstruction task.
  arXiv  Detail & Related papers  (2022-03-31T16:51:25Z)
• Revisiting Transformation Invariant Geometric Deep Learning: An Initial Representation Perspective [38.314914702299056]
  We propose Transformation In Neural Networks (TinvNN), a straightforward and general plug-in for geometric data.<n>Specifically, we realize transformation invariant and distance-preserving initial point representations by modifying multi-dimensional scaling.<n>We prove that TinvNN can strictly guarantee transformation invariant, being general and flexible enough to be combined with the existing neural networks.
  arXiv  Detail & Related papers  (2021-12-23T03:52:33Z)
• Mitigating Generation Shifts for Generalized Zero-Shot Learning [52.98182124310114]
  Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not observable during training. We propose a novel Generation Shifts Mitigating Flow framework for learning unseen data synthesis efficiently and effectively. Experimental results demonstrate that GSMFlow achieves state-of-the-art recognition performance in both conventional and generalized zero-shot settings.
  arXiv  Detail & Related papers  (2021-07-07T11:43:59Z)
• Symmetry-driven graph neural networks [1.713291434132985]
  We introduce two graph network architectures that are equivariant to several types of transformations affecting the node coordinates. We demonstrate these capabilities on a synthetic dataset composed of $n$-dimensional geometric objects.
  arXiv  Detail & Related papers  (2021-05-28T18:54:12Z)
• Spherical Feature Transform for Deep Metric Learning [58.35971328774927]
  This work proposes a novel spherical feature transform approach. It relaxes the assumption of identical covariance between classes to an assumption of similar covariances of different classes on a hypersphere. We provide a simple and effective training method, and in depth analysis on the relation between the two different transforms.
  arXiv  Detail & Related papers  (2020-08-04T11:32:23Z)

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.