{"id":770,"date":"2023-12-04T19:33:48","date_gmt":"2023-12-04T11:33:48","guid":{"rendered":"https:\/\/511cvlab.sinkers.cn\/?p=770"},"modified":"2025-10-17T17:22:12","modified_gmt":"2025-10-17T09:22:12","slug":"rdfnet","status":"publish","type":"post","link":"https:\/\/cv.nirc.top\/zh\/2023\/rdfnet\/","title":{"rendered":"Region-Aware Dynamic Filtering Network for 3D Hand Reconstruction"},"content":{"rendered":"<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-f00c8009 wp-block-group-is-layout-constrained\" style=\"padding-right:5%;padding-left:5%\">\n<p class=\"has-text-align-center\"><sup>a<\/sup>State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications<\/p>\n\n\n\n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-block-group-is-layout-constrained\">\n    <div\n        class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-1 wp-block-buttons-is-layout-flex\">\n        <div class=\"wp-block-button\" style=\"line-height: 1.5;\">\n            <a class=\"wp-block-button__link wp-element-button\"  target=\"_blank\"\n                href=\"https:\/\/www.researchgate.net\/publication\/374300347_Region-Aware_Dynamic_Filtering_Network_for_3D_Hand_Reconstruction\"\n                style=\"padding-right: var(--wp--preset--spacing--40); padding-left: var(--wp--preset--spacing--40); display: flex; align-items: center; gap: 8px;\">\n                <div>\n                    <svg class=\"svg-inline--fa fa-file-pdf fa-w-12\" aria-hidden=\"true\" focusable=\"false\"\n                        data-prefix=\"fas\" data-icon=\"file-pdf\" role=\"img\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"\n                        viewbox=\"0 0 384 512\" style=\"height: 1em; width: 1em;\">\n                        <path fill=\"#FFFFFF\"\n                            d=\"M181.9 256.1c-5-16-4.9-46.9-2-46.9 8.4 0 7.6 36.9 2 46.9zm-1.7 47.2c-7.7 20.2-17.3 43.3-28.4 62.7 18.3-7 39-17.2 62.9-21.9-12.7-9.6-24.9-23.4-34.5-40.8zM86.1 428.1c0 .8 13.2-5.4 34.9-40.2-6.7 6.3-29.1 24.5-34.9 40.2zM248 160h136v328c0 13.3-10.7 24-24 24H24c-13.3 0-24-10.7-24-24V24C0 10.7 10.7 0 24 0h200v136c0 13.2 10.8 24 24 24zm-8 171.8c-20-12.2-33.3-29-42.7-53.8 4.5-18.5 11.6-46.6 6.2-64.2-4.7-29.4-42.4-26.5-47.8-6.8-5 18.3-.4 44.1 8.1 77-11.6 27.6-28.7 64.6-40.8 85.8-.1 0-.1.1-.2.1-27.1 13.9-73.6 44.5-54.5 68 5.6 6.9 16 10 21.5 10 17.9 0 35.7-18 61.1-61.8 25.8-8.5 54.1-19.1 79-23.2 21.7 11.8 47.1 19.5 64 19.5 29.2 0 31.2-32 19.7-43.4-13.9-13.6-54.3-9.7-73.6-7.2zM377 105L279 7c-4.5-4.5-10.6-7-17-7h-6v128h128v-6.1c0-6.3-2.5-12.4-7-16.9zm-74.1 255.3c4.1-2.7-2.5-11.9-42.8-9 37.1 15.8 42.8 9 42.8 9z\">\n                        <\/path>\n                    <\/svg>\n                <\/div>\n                <div>Paper<\/div>\n            <\/a>\n        <\/div>\n\n        \n    <\/div>\n<\/div>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69f8bfebe764c&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69f8bfebe764c\" class=\"wp-block-image aligncenter size-large is-resized wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"345\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/511cvlab.sinkers.cn\/wp-content\/uploads\/2024\/12\/image-1-1024x345.png\" alt=\"\" class=\"wp-image-771\" style=\"width:700px\" srcset=\"https:\/\/cv.nirc.top\/wp-content\/uploads\/2024\/12\/image-1-1024x345.png 1024w, https:\/\/cv.nirc.top\/wp-content\/uploads\/2024\/12\/image-1-300x101.png 300w, https:\/\/cv.nirc.top\/wp-content\/uploads\/2024\/12\/image-1-768x259.png 768w, https:\/\/cv.nirc.top\/wp-content\/uploads\/2024\/12\/image-1.png 1506w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"\u653e\u5927\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewbox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">Illustrations of different occlusion degree of regions. Severely occluded regions are denoted by blue box, while the visible regions are denoted by green box. For the visible region, model focuses on the local region pattern by using the small kernels that conform to the finger position distribution. For occluded regions, larger convolution kernels are used to obtain information from adjacent regions.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Abstract<\/h2>\n\n\n\n<p class=\"text-justify\">3D hand reconstruction from RGB image has attracted a lot of attention due to its crucial role in human-computer interaction. Nevertheless, it is still challenging to perform 3D hand reconstruction under conditions of hand-object interaction due to severe mutual occlusion. Previous methods usually adopt fixed convolution kernel to extract features. We argue that simply sharing the static filter for all regions is impertinent, given that the occlusion degree varies across different regions, resulting in inconsistent visual representations. To address this issue, we proposed Region-aware Dynamic Filtering Network (RDFNet), which dynamically generates convolution kernels based on the features of different regions, thereby adaptively extracting region-related information. Furthermore, we introduce a dynamic receptive field selection mechanism to determine the most appropriate scale for the convolution kernel. For the severely occluded regions, larger receptive field is needed to capture semantic-related features, while the visible regions are mainly concerned with their own local pattern to accumulate spatial-related features and avoid the interference of irrelevant information. Our proposed RDFNet outperforms state-of-the-art methods by a large margin on several challenging hand-object interaction datasets.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Overview<\/h2>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69f8bfebe7b1b&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69f8bfebe7b1b\" class=\"wp-block-image size-large wp-lightbox-container\"><img decoding=\"async\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/sinkers-pic.oss-cn-beijing.aliyuncs.com\/img\/rdf-WechatIMG141.jpg\" alt=\"\"\/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"\u653e\u5927\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewbox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">The overall architecture of RDFNet. PR denotes the pixel-wise representation, RKFE denotes the region kernel feature extraction and RDF denotes the region-aware dynamic filtering module.<\/figcaption><\/figure>\n\n\n\n<p class=\"text-justify\">RDFNet first extracts initial features from the backbone. Then, it take the initial features and perform region-aware dynamic feature refinement. Finally, decoder generates the MANO shape and pose parameters, which are propagated into the MANO layer and generate the final hand reconstruction result.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Bibtex<\/h2>\n\n\n\n<pre class=\"wp-block-code\"><code>@inproceedings{chen2023region,\n  title={Region-Aware Dynamic Filtering Network for 3D Hand Reconstruction.},\n  author={Chen, Yuchen and Ren, Pengfei and Wang, Jingyu and Sun, Haifeng and Qi, Qi and Wang, Jing and Liao, Jianxin},\n  booktitle={ECAI},\n  pages={437--444},\n  year={2023}\n}<\/code><\/pre>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>aState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications Paper Abstract 3D hand reconstruction from RGB image has attracted a lot of attention due to its crucial role in human-computer interaction. Nevertheless, it is still challenging to perform 3D hand reconstruction under conditions of hand-object interaction due to severe mutual [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":771,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[17],"tags":[7],"class_list":["post-770","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-rgb-based-3d-hand-pose-estimation","tag-oral"],"acf":{"writer":{"simple_value_formatted":"<code><em>This data type is not supported! 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