Monocular Models are Strong Learners for Multi-View Human Mesh Recovery

• URL: http://arxiv.org/abs/2603.20391v1
• Date: Fri, 20 Mar 2026 18:10:13 GMT
• Title: Monocular Models are Strong Learners for Multi-View Human Mesh Recovery
• Abstract summary: Multi-view human mesh recovery (HMR) is broadly deployed in diverse domains where high accuracy and strong generalization are essential.<n>Existing approaches can be broadly grouped into geometry-based and learning-based methods.<n>We propose a training-free framework that leverages pretrained single-view HMR models as strong priors, eliminating the need for multi-view training data.
• Score: 19.66264380616788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view human mesh recovery (HMR) is broadly deployed in diverse domains where high accuracy and strong generalization are essential. Existing approaches can be broadly grouped into geometry-based and learning-based methods. However, geometry-based methods (e.g., triangulation) rely on cumbersome camera calibration, while learning-based approaches often generalize poorly to unseen camera configurations due to the lack of multi-view training data, limiting their performance in real-world scenarios. To enable calibration-free reconstruction that generalizes to arbitrary camera setups, we propose a training-free framework that leverages pretrained single-view HMR models as strong priors, eliminating the need for multi-view training data. Our method first constructs a robust and consistent multi-view initialization from single-view predictions, and then refines it via test-time optimization guided by multi-view consistency and anatomical constraints. Extensive experiments demonstrate state-of-the-art performance on standard benchmarks, surpassing multi-view models trained with explicit multi-view supervision.

Related papers

• CalibAnyView: Beyond Single-View Camera Calibration in the Wild [59.66873936532375]
  Camera calibration is a fundamental prerequisite for reliable geometric perception.<n>Recent learning-based methods have shown promising results for single-view calibration, but inherently neglect geometric consistency across multiple views.<n>We introduce CalibAnyView, a unified formulation that supports an arbitrary number of input views.
  arXiv  Detail & Related papers  (2026-05-14T09:32:12Z)
• ERNIE 5.0 Technical Report [244.36480708815316]
  ERNIE 5.0 is a unified autoregressive foundation model for unified multimodal understanding and generation across text, image, video, and audio.<n>To address practical challenges in large-scale deployment under diverse resource constraints, ERNIE 5.0 adopts a novel elastic training paradigm.<n>We show that ERNIE 5.0 achieves strong and balanced performance across multiple modalities.
  arXiv  Detail & Related papers  (2026-02-04T16:18:15Z)
• Twin Restricted Kernel Machines for Multiview Classification [10.208788616684162]
  Multi-view learning (MVL) focuses on improving generalization performance by leveraging complementary information from multiple perspectives or views.<n>We introduce the multiview twin restricted kernel machine (TMvRKM), a novel model that integrates the strengths of kernel machines with the multiview framework.<n>The proposed TMvRKM model is rigorously tested on UCI, KEEL, and AwA benchmark datasets.
  arXiv  Detail & Related papers  (2025-12-12T03:54:19Z)
• Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition [14.020214078011515]
  We address multi-reference visual place recognition (VPR), where reference sets captured under varying conditions are used to improve localisation performance.<n>We propose a training-free, agnostic approach that jointly models places using multiple reference descriptors via matrix decomposition into basis representations.<n>On multi-appearance data, our method improves Recall@1 by up to 18% over single-reference and outperforms multi-reference baselines across appearance and viewpoint changes.
  arXiv  Detail & Related papers  (2025-10-20T16:50:03Z)
• Visual Odometry with Transformers [68.453547770334]
  We introduce Visual odometry Transformer (VoT), which processes sequences of monocular frames by extracting features.<n>Unlike prior methods, VoT directly predicts camera motion without estimating dense geometry and relies solely on camera poses for supervision.<n>VoT scales effectively with larger datasets, benefits substantially from stronger pre-trained backbones, generalizes across diverse camera motions and calibration settings, and outperforms traditional methods while running more than 3 times faster.
  arXiv  Detail & Related papers  (2025-10-02T17:00:14Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory [14.562893624131531]
  This paper pioneered the introduction of PAC-Bayes generalized bound theory into the training analysis of specific models. We proposed an adaptive training method which can greatly improve the image manipulation ability of multi-scale GANs. In particular, for the image super-resolution restoration task, the multi-scale GAN model trained by the proposed method achieves a 100% reduction in natural image quality evaluator (NIQE) and a 60% reduction in root mean squared error (RMSE)
  arXiv  Detail & Related papers  (2022-11-30T07:11:56Z)
• Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information [77.80071279597665]
  We propose an all-in-one single-stage pre-training approach, named Maximizing Multi-modal Mutual Information Pre-training (M3I Pre-training) Our approach achieves better performance than previous pre-training methods on various vision benchmarks, including ImageNet classification, object detection, LVIS long-tailed object detection, and ADE20k semantic segmentation.
  arXiv  Detail & Related papers  (2022-11-17T18:59:49Z)
• A Unifying Multi-sampling-ratio CS-MRI Framework With Two-grid-cycle Correction and Geometric Prior Distillation [7.643154460109723]
  We propose a unifying deep unfolding multi-sampling-ratio CS-MRI framework, by merging advantages of model-based and deep learning-based methods. Inspired by multigrid algorithm, we first embed the CS-MRI-based optimization algorithm into correction-distillation scheme. We employ a condition module to learn adaptively step-length and noise level from compressive sampling ratio in every stage.
  arXiv  Detail & Related papers  (2022-05-14T13:36:27Z)
• Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints [80.60538408386016]
  Estimating relative camera poses from consecutive frames is a fundamental problem in visual odometry. We propose an end-to-end trainable framework consisting of learnable modules for detection, feature extraction, matching and outlier rejection.
  arXiv  Detail & Related papers  (2020-07-29T21:41:31Z)

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.