拟投顶会论文的详细提纲 Multi-View Human Mesh Recovery with Segmentation Masks and 3D Key-point Guidance

Title: "Multi-View Human Mesh Recovery with Segmentation Masks and 3D Key-point Guidance"

Abstract:

This paper addresses the challenge of accurately recovering 3D human meshes from multi-view images, particularly in the presence of occlusions. We propose a novel framework that leverages the complementary information provided by segmentation masks and 3D key-points to enhance the reconstruction process. Our approach integrates a multi-view convolutional neural network (CNN) with a graph convolutional network (GCN) to effectively fuse the multi-modal input data. The CNN extracts features from the multi-view images and segmentation masks, while the GCN incorporates the 3D key-point information to refine the mesh topology and geometry. We introduce a novel loss function that combines a multi-view consistency term, a segmentation mask alignment term, and a 3D key-point distance term to ensure accurate and robust mesh recovery. Extensive experiments on benchmark datasets demonstrate that our method outperforms state-of-the-art approaches in terms of both accuracy and robustness to occlusions. Our approach has significant implications for various applications, including human-computer interaction, virtual reality, and 3D animation.

Keywords: 3D human mesh recovery, multi-view images, segmentation masks, 3D key-points, convolutional neural networks, graph convolutional networks, occlusion handling.

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TOC:

• Introduction
  • 1.1 Background and Motivation
    • 1.1.1 3D Human Mesh Recovery in Computer Vision
    • 1.1.2 Applications and Challenges
  • 1.2 Problem Statement and Challenges
    • 1.2.1 Limitations of Existing Methods
    • 1.2.2 Occlusion Handling
  • 1.3 Proposed Approach and Contributions
    • 1.3.1 Multi-view Fusion with Segmentation Masks
    • 1.3.2 3D Key-point Guidance
    • 1.3.3 Novel Loss Function
  • 1.4 Paper Organization
• Related Work
  • 2.1 3D Human Mesh Recovery from Multi-view Images
    • 2.1.1 Volumetric Methods
    • 2.1.2 Model-based Methods
  • 2.2 Segmentation Masks for Human Mesh Refinement
    • 2.2.1 Mask-guided Feature Extraction
    • 2.2.2 Mesh Deformation with Mask Constraints
  • 2.3 3D Key-point Guidance for Pose and Shape Estimation
    • 2.3.1 Key-point-based Pose Estimation
    • 2.3.2 Shape Reconstruction from Key-points
• Proposed Method
  • 3.1 Multi-view CNN for Feature Extraction
    • 3.1.1 Network Architecture
    • 3.1.2 Feature Fusion across Views
  • 3.2 GCN for 3D Key-point Integration
    • 3.2.1 Graph Construction
    • 3.2.2 Message Passing and Feature Update
  • 3.3 Fusion Module for Multi-modal Data Aggregation
    • 3.3.1 Feature Concatenation and Attention
  • 3.4 Loss Function Design
    • 3.4.1 Multi-view Consistency Loss
    • 3.4.2 Segmentation Mask Alignment Loss
    • 3.4.3 3D Key-point Distance Loss
• Experiments
  • 4.1 Datasets and Evaluation Metrics
    • 4.1.1 Human3.6M Dataset
    • 4.1.2 CMU Panoptic Dataset
    • 4.1.3 Evaluation Metrics (MPJPE, PA-MPJPE, MPVE)
  • 4.2 Implementation Details
    • 4.2.1 Training Setup and Hyperparameters
    • 4.2.2 Data Augmentation
  • 4.3 Quantitative Results and Analysis
    • 4.3.1 Comparison with State-of-the-art Methods
    • 4.3.2 Performance under Different Occlusion Levels
  • 4.4 Qualitative Results and Visualization
    • 4.4.1 Visual Comparison of Reconstructed Meshes
• Discussion
  • 5.1 Ablation Studies on Different Components
    • 5.1.1 Effect of Segmentation Masks
    • 5.1.2 Impact of 3D Key-point Guidance
  • 5.2 Limitations and Future Work
    • 5.2.1 Generalization to Unseen Poses and Shapes
    • 5.2.2 Real-time Performance
• Conclusion