Gait Recognition in the Wild with Dense 3D Representations and A Benchmark

• URL: http://arxiv.org/abs/2204.02569v1
• Date: Wed, 6 Apr 2022 03:54:06 GMT
• Title: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark
• Authors: Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei
• Abstract summary: Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. This paper aims to explore dense 3D representations for gait recognition in the wild. We build the first large-scale 3D representation-based gait recognition dataset, named Gait3D.
• Score: 86.68648536257588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. However, humans live and walk in the unconstrained 3D space, so projecting the 3D human body onto the 2D plane will discard a lot of crucial information like the viewpoint, shape, and dynamics for gait recognition. Therefore, this paper aims to explore dense 3D representations for gait recognition in the wild, which is a practical yet neglected problem. In particular, we propose a novel framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the human body for gait recognition, named SMPLGait. Our framework has two elaborately-designed branches of which one extracts appearance features from silhouettes, the other learns knowledge of 3D viewpoints and shapes from the 3D SMPL model. In addition, due to the lack of suitable datasets, we build the first large-scale 3D representation-based gait recognition dataset, named Gait3D. It contains 4,000 subjects and over 25,000 sequences extracted from 39 cameras in an unconstrained indoor scene. More importantly, it provides 3D SMPL models recovered from video frames which can provide dense 3D information of body shape, viewpoint, and dynamics. Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild. The code and dataset are available at https://gait3d.github.io.

Related papers

• Learning 3D Representations from Procedural 3D Programs [6.915871213703219]
  Self-supervised learning has emerged as a promising approach for acquiring transferable 3D representations from unlabeled 3D point clouds. We propose learning 3D representations from procedural 3D programs that automatically generate 3D shapes using simple primitives and augmentations.
  arXiv  Detail & Related papers  (2024-11-25T18:59:57Z)
• DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data [50.164670363633704]
  We present DIRECT-3D, a diffusion-based 3D generative model for creating high-quality 3D assets from text prompts. Our model is directly trained on extensive noisy and unaligned in-the-wild' 3D assets. We achieve state-of-the-art performance in both single-class generation and text-to-3D generation.
  arXiv  Detail & Related papers  (2024-06-06T17:58:15Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [114.47216525866435]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• Uni3D: Exploring Unified 3D Representation at Scale [66.26710717073372]
  We present Uni3D, a 3D foundation model to explore the unified 3D representation at scale. Uni3D uses a 2D ViT end-to-end pretrained to align the 3D point cloud features with the image-text aligned features. We show that the strong Uni3D representation also enables applications such as 3D painting and retrieval in the wild.
  arXiv  Detail & Related papers  (2023-10-10T16:49:21Z)
• AG3D: Learning to Generate 3D Avatars from 2D Image Collections [96.28021214088746]
  We propose a new adversarial generative model of realistic 3D people from 2D images. Our method captures shape and deformation of the body and loose clothing by adopting a holistic 3D generator. We experimentally find that our method outperforms previous 3D- and articulation-aware methods in terms of geometry and appearance.
  arXiv  Detail & Related papers  (2023-05-03T17:56:24Z)
• TANDEM3D: Active Tactile Exploration for 3D Object Recognition [16.548376556543015]
  We propose TANDEM3D, a method that applies a co-training framework for 3D object recognition with tactile signals. TANDEM3D is based on a novel encoder that builds 3D object representation from contact positions and normals using PointNet++. Our method is trained entirely in simulation and validated with real-world experiments.
  arXiv  Detail & Related papers  (2022-09-19T05:54:26Z)
• Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild [32.05421669957098]
  Large datasets and scalable solutions have led to unprecedented advances in 2D recognition. We revisit the task of 3D object detection by introducing a large benchmark, called Omni3D. We show that Cube R-CNN outperforms prior works on the larger Omni3D and existing benchmarks.
  arXiv  Detail & Related papers  (2022-07-21T17:56:22Z)
• Parameter-Efficient Person Re-identification in the 3D Space [51.092669618679615]
  We project 2D images to a 3D space and introduce a novel parameter-efficient Omni-scale Graph Network (OG-Net) to learn the pedestrian representation directly from 3D point clouds. OG-Net effectively exploits the local information provided by sparse 3D points and takes advantage of the structure and appearance information in a coherent manner. We are among the first attempts to conduct person re-identification in the 3D space.
  arXiv  Detail & Related papers  (2020-06-08T13:20:33Z)

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.