HybridGait: A Benchmark for Spatial-Temporal Cloth-Changing Gait Recognition with Hybrid Explorations

• URL: http://arxiv.org/abs/2401.00271v1
• Date: Sat, 30 Dec 2023 16:12:13 GMT
• Title: HybridGait: A Benchmark for Spatial-Temporal Cloth-Changing Gait Recognition with Hybrid Explorations
• Authors: Yilan Dong, Chunlin Yu, Ruiyang Ha, Ye Shi, Yuexin Ma, Lan Xu, Yanwei Fu, Jingya Wang
• Abstract summary: We propose the first in-the-wild benchmark CCGait for cloth-changing gait recognition. We exploit both temporal dynamics and the projected 2D information of 3D human meshes. Our contributions are twofold: we provide a challenging benchmark CCGait that captures realistic appearance changes across an expanded and space.
• Score: 66.5809637340079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing gait recognition benchmarks mostly include minor clothing variations in the laboratory environments, but lack persistent changes in appearance over time and space. In this paper, we propose the first in-the-wild benchmark CCGait for cloth-changing gait recognition, which incorporates diverse clothing changes, indoor and outdoor scenes, and multi-modal statistics over 92 days. To further address the coupling effect of clothing and viewpoint variations, we propose a hybrid approach HybridGait that exploits both temporal dynamics and the projected 2D information of 3D human meshes. Specifically, we introduce a Canonical Alignment Spatial-Temporal Transformer (CA-STT) module to encode human joint position-aware features, and fully exploit 3D dense priors via a Silhouette-guided Deformation with 3D-2D Appearance Projection (SilD) strategy. Our contributions are twofold: we provide a challenging benchmark CCGait that captures realistic appearance changes across an expanded and space, and we propose a hybrid framework HybridGait that outperforms prior works on CCGait and Gait3D benchmarks. Our project page is available at https://github.com/HCVLab/HybridGait.

Related papers

• GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency [50.11520458252128]
  Existing 3D affordance learning methods struggle with generalization and robustness due to limited annotated data. We propose GEAL, a novel framework designed to enhance the generalization and robustness of 3D affordance learning by leveraging large-scale pre-trained 2D models. GEAL consistently outperforms existing methods across seen and novel object categories, as well as corrupted data.
  arXiv  Detail & Related papers  (2024-12-12T17:59:03Z)
• HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting [47.67153284714988]
  We propose a novel hybrid representation, termed as HybridGS, using 2D Gaussians for transient objects per image. We also propose a straightforward yet effective multi-stage training strategy to ensure robust training and high-quality view synthesis. Experiments on benchmark datasets show our state-of-the-art performance of novel view synthesis in both indoor and outdoor scenes.
  arXiv  Detail & Related papers  (2024-12-05T03:20:35Z)
• Graph and Skipped Transformer: Exploiting Spatial and Temporal Modeling Capacities for Efficient 3D Human Pose Estimation [36.93661496405653]
  We take a global approach to exploit Transformer-temporal information with a concise Graph and Skipped Transformer architecture. Specifically, in 3D pose stage, coarse-grained body parts are deployed to construct a fully data-driven adaptive model. Experiments are conducted on Human3.6M, MPI-INF-3DHP and Human-Eva benchmarks.
  arXiv  Detail & Related papers  (2024-07-03T10:42:09Z)
• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)
• Geometry-Biased Transformer for Robust Multi-View 3D Human Pose Reconstruction [3.069335774032178]
  We propose a novel encoder-decoder Transformer architecture to estimate 3D poses from multi-view 2D pose sequences. We conduct experiments on three benchmark public datasets, Human3.6M, CMU Panoptic and Occlusion-Persons.
  arXiv  Detail & Related papers  (2023-12-28T16:30:05Z)
• Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection [116.21529970404653]
  We introduce SG2HOI+, a unified one-step model based on the Transformer architecture. Our approach employs two interactive hierarchical Transformers to seamlessly unify the tasks of SGG and HOI detection. Our approach achieves competitive performance when compared to state-of-the-art HOI methods.
  arXiv  Detail & Related papers  (2023-11-03T07:25:57Z)
• MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video [75.23812405203778]
  Recent solutions have been introduced to estimate 3D human pose from 2D keypoint sequence by considering body joints among all frames globally to learn-temporal correlation. We propose Mix Mix, which has temporal transformer block to separately model the temporal motion of each joint and a transformer block inter-joint spatial correlation. In addition, the network output is extended from the central frame to entire frames of input video, improving the coherence between the input and output benchmarks.
  arXiv  Detail & Related papers  (2022-03-02T04:20:59Z)
• JointsGait:A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping [6.851535012702575]
  We research on using 2D joints to recognize gait in this paper. JointsGait is put forward to extract gait information from 2D human body joints.
  arXiv  Detail & Related papers  (2020-04-27T08:30:37Z)

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.