{"id":782,"date":"2023-12-02T20:01:39","date_gmt":"2023-12-02T12:01:39","guid":{"rendered":"https:\/\/511cvlab.sinkers.cn\/?p=782"},"modified":"2025-10-17T17:16:50","modified_gmt":"2025-10-17T09:16:50","slug":"smr","status":"publish","type":"post","link":"https:\/\/cv.nirc.top\/zh\/2023\/smr\/","title":{"rendered":"SMR: Spatial-Guided Model-Based Regression for 3D Hand Pose and Mesh Reconstruction"},"content":{"rendered":"\n
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Paper<\/div>\n <\/a>\n <\/div>\n\n \n\n \n <\/div>\n<\/div>\n\n\n\n
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llustration of 3D hand reconstruction from a monocular RGB image. This task takes an RGB image as input and outputs the 3D hand sparse hand joints and dense mesh vertices.<\/figcaption><\/figure>\n\n\n\n

Abstract<\/h2>\n\n\n\n

3D hand reconstruction is an important technique for human-computer interaction. Interactive experience depends on the accuracy, efficiency, and robustness of the algorithm. Therefore, in this paper, we first propose a balanced framework called spatial-aware regression (SAR) <\/a>to achieve precise and fast reconstruction. SAR can bridge convolutional networks and graph-structure networks more effectively than existing frameworks to fully exploit extracted spatial information using a novel spatial-aware initial graph building module. In addition, SAR uses adaptive-GCN to make keypoints interact efficiently and effectively; and regresses 2.5D belief maps to characterize uncertainty. SAR is highly flexible because it can predict an arbitrary number of keypoints and apply pose-guided refinement for coarse to fine regression. To produce more rational results for challenging cases and mitigate 3D label reliance, we also propose a more robust model-based framework called spatial-guided model-based regression (SMR) that is based on SAR. There are two critical designs of SMR: 1) it uses SAR to enhance the features with pose information to help the regression of hand model parameters; and 2) it regresses parameters in a spatially aware manner that is similar to SAR. Experiments demonstrate that the proposed frameworks surpass existing fully-supervised approaches on the FreiHAND, HO-3D, RHD, and STB datasets. Also, the performances of the proposed frameworks under weakly\/self-supervised settings outperform other competitors. Meanwhile, the proposed frameworks are accurate and efficient.<\/p>\n\n\n\n

Overview<\/h2>\n\n\n\n
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Illustration of the pipeline of (a) spatial-aware regression (SAR), (b) spatial-guided model-based regression (SMR), and (c) SAR-Head.<\/figcaption><\/figure>\n\n\n\n

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1\ufe0f\u20e3 SAR first uses CNNs (E-1 and E-2) to extract feature maps from the input image. Then, SAR feeds the feature maps to SAR-Head (N=799, S=32) to regress the pose (21 keypoints) and mesh (778 keypoints) coordinates by decoding from the predicted 2.5D belief maps of resolution 32 \u00d7 32. Finally, SAR combines the 2.5D pose belief maps with the middle-level features from CNNs to the following stage for obtaining refined results.<\/p>\n\n\n\n

2\ufe0f\u20e3 SMR first uses CNNs to extract feature maps. Then, SMR uses a SAR-Head (N=21, S=8) to obtain the initial pose from the feature maps. Then, SMR combines the low-resolution 2.5D belief maps of the initial pose and the feature maps as the pose-guided features. Then, SMR feeds pose-guided features to SAPR-Head to regress the hand model (MANO) parameters (\u03b8 and \u03b2). SMR then uses the MANO layer to decode those parameters to a 3D hand mesh. The 3D hand pose is obtained by a linear regressor from the 3D hand mesh. The 2D hand mesh and pose are obtained by the weak-perspective camera projection from the 3D hand mesh and pose using the predicted camera parameters (\u03c0 ). In addition, SMR also uses another SAR-Head (N=21, S=32) to obtain high-resolution 2.5D pose belief maps from the pose-guided features for multistage refinement.<\/p>\n\n\n\n

3\ufe0f\u20e3 SAR-Head uses the SAIGB module to build an initial feature graph with every keypoint containing several feature maps from CNNs. Then, it uses the GBBMR module to map the initial features to belief maps with Adaptive-GCN. N and S for SAR-Head denote targeting regressing keypoints number and the size of the predicted belief maps.<\/p>\n\n\n\n

Qualitative Results<\/h2>\n\n\n\n

Visual results comparisons on FreiHAND<\/strong>:<\/p>\n\n\n\n

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Cross-dataset visual results comparison:<\/p>\n<\/div>\n\n\n\n

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The visual results of SMR and the corresponding failure cases of SAR. Except under those typically difficult situations, SAR and SMR achieve similar performance to the result.<\/p>\n\n\n\n

Visual results of SAR on the FreiHAND<\/strong>, HO-3D<\/strong>, RHD<\/strong>, and STB<\/strong> datasets:<\/p>\n\n\n\n

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Compare with SOTA<\/h2>\n\n\n\n
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(Click \u201c<\/p>\n\n\n\n

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\u201d for details)<\/p>\n<\/div>\n\n\n\n

Fully-Supervised Learning<\/h3>\n\n\n\n
FreiHAND<\/strong>: SAR can achieve a good balance of accuracy and efficiency. SMR outperforms all other methods in terms of all the metrics mentioned above.<\/summary>\n
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Comparisons with existing fully-supervised methods on the FreiHAND dataset. \u2217 denotes using testing augmentation.<\/figcaption><\/figure>\n<\/details>\n\n\n\n
HO-3D<\/strong>: SMR outperforms SAR by a large margin. SAR can be overfitted more easily than SMR. SMR can obtain more robust results when hand-object occlusions occur due to the hand model information.<\/summary>\n
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Comparisons with existing fully-supervised methods on the HO-3D dataset. + denotes training with the weights pre-trained on FreiHAND. v3 denotes the results on the HO-3D-v3 dataset.<\/figcaption><\/figure>\n<\/details>\n\n\n\n
RHD & STB<\/strong>: SAR and SMR can achieve good performance on the RHD dataset and STB dataset.<\/summary>\n
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Comparisons of 3D PCK on the RHD dataset.<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Comparisons of 3D PCK on the STB dataset.<\/figcaption><\/figure>\n<\/div>\n<\/div>\n<\/details>\n\n\n\n

Weakly\/Self-Supervised Learning<\/h3>\n\n\n\n
Because SMR uses the hand model as the decoder, it can be trained under label insufficient situations. Therefore, we also compare the proposed weakly\/self-supervised performance with other methods on the FreiHAND dataset.<\/summary>\n

\u2705 When only using 2D labels for training, the performance surpasses recent competitors by a large margin.
\u2705 When conducting self-supervised training without using any manual annotations, the proposed method still outperforms S2<\/sup>HAND.<\/p>\n\n\n\n

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Comparisons with existing weakly\/self-supervised methods on the FreiHAND dataset. \u2020 denotes using additional synthetic data with 3D annotations for training.<\/figcaption><\/figure>\n<\/details>\n\n\n\n

All these results demonstrate that a well-designed model-based framework can obtain more robust fully-supervised reconstruction and achieve remarkable performance under insufficient label situations.<\/p>\n\n\n\n

Conclusion<\/h2>\n\n\n\n

We propose two frameworks for 3D hand reconstruction from a monocular RGB image, including SAR<\/strong> and SMR<\/strong>. <\/p>\n\n\n\n

1\ufe0f\u20e3 SAR<\/strong> achieves a good balance of accuracy and efficiency for keypoint localization tasks with the help of spatial-aware initial graph building, GCN-based belief maps regression, and pose-guided refinement. <\/p>\n\n\n\n

2\ufe0f\u20e3 In addition to SAR, SMR<\/strong> incorporates the hand model<\/strong> to provide hand prior information and enhances the model-based method<\/strong> with pose-guided features and spatial-aware parameter regression, which can address challenging cases and label insufficiency. <\/p>\n\n\n\n

Extensive experiments on four public benchmarks show that the proposed methods can achieve good performance under fully\/weakly\/self-supervised settings with a high inference speed.<\/p>\n\n\n\n

Bibtex<\/h2>\n\n\n\n
@article{sun2023smr,\n  title={SMR: Spatial-Guided Model-Based Regression for 3D Hand Pose and Mesh Reconstruction},\n  author={Sun, Haifeng and Zheng, Xiaozheng and Ren, Pengfei and Wang, Jingyu and Qi, Qi and Liao, Jianxin},\n  journal={IEEE Transactions on Circuits and Systems for Video Technology},\n  volume={34},\n  number={1},\n  pages={299--314},\n  year={2023},\n  publisher={IEEE}\n}<\/code><\/pre>\n<\/div>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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