{"id":2031,"date":"2025-03-01T16:59:00","date_gmt":"2025-03-01T08:59:00","guid":{"rendered":"https:\/\/511cvlab.sinkers.cn\/?p=2031"},"modified":"2025-11-10T17:13:52","modified_gmt":"2025-11-10T09:13:52","slug":"temp-lowres-hand","status":"publish","type":"post","link":"https:\/\/cv.nirc.top\/zh\/2025\/temp-lowres-hand\/","title":{"rendered":"Pose-Guided Temporal Enhancement for Robust Low-Resolution Hand Reconstruction"},"content":{"rendered":"<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-8296f85a wp-block-group-is-layout-constrained\" style=\"padding-right:5%;padding-left:5%\">\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69f8bda906d2d&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69f8bda906d2d\" class=\"wp-block-image aligncenter size-large is-resized wp-lightbox-container\" style=\"margin-top:var(--wp--preset--spacing--50)\"><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\/25CVPR-teaser.png\" alt=\"\" style=\"width:660px\"\/><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\">Different way to utilizing joint features. a) Directly reconstruct hand from joint features, relying solely on sparse features without spatial context. b) Fusing joint and visual features through cross-attention, which avoids representation discrepancies but neglects the hand\u2019s structural priors. c) Using triplane features to bridge joint and visual features by decoding structural joint in\u0002formation into dense representations that enhance visual features.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Abstract<\/h2>\n\n\n\n<p>3D hand reconstruction is essential in non-contact human-computer interaction applications, but existing methods struggle with low-resolution images, which occur in slightly distant interactive scenes. Leveraging temporal information can mitigate the limitations of individual low-resolution images that lack detailed appearance information, thereby enhancing the robustness and accuracy of hand reconstruction. Existing temporal methods typically use joint features to represent temporal information, avoiding interference from redundant background information. However, joint features excessively disregard the spatial context of visual features, limiting hand reconstruction accuracy. We propose to integrate temporal joint features with visual features to construct a robust low-resolution visual representation. We introduce Triplane Features, a dense representation with 3D spatial awareness, to bridge the gap between the joint features and visual features that are misaligned in terms of representation form and semantics. Triplane Features are obtained by orthogonally projecting the joint features, embedding hand structure information into the 3D spatial context. Furthermore, we compress the spatial information of the three planes into a 2D dense feature thourgh Spatial-Aware Fusion to enhance the visual features. By using enhanced visual features enriched with temporal information for hand reconstruction, our method achieves competitive performance at much lower resolutions compared to state-of-the-art methods operating at high resolution on DexYCB, HanCo and H2O.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Overview<\/h2>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69f8bda907581&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69f8bda907581\" 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\/25CVPR-overview.png\" 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\">Overview of Our Framework. We first construct joint features from the sampled past frame features and 2D poses using a Spatial-Temporal Transformer (a), capturing the temporal dynamics and spatial relationships between joints. We then use these joint features to enhance the visual features of the current frame (b) by converting them into spatial-aware dense features through Tripalne Feature Encoding (c). In (c), we fuse different joint features (from a) using pose-aware tokens (from b), and project them into three orthogonal planes to obtain the Triplane features. These features are then compressed into a single plane through space-aware fusion.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Qualitative Results<\/h2>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69f8bda90793f&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69f8bda90793f\" 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\/25CVPR-Qualitative.png\" 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\">Qualitative Comparison Results on DexYCB. Regions with significant prediction errors are marked with red circles.<\/figcaption><\/figure>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69f8bda907cb6&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69f8bda907cb6\" 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\/25CVPR-wild.png\" 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\">Qualitative Results in the wild. The original images were captured at a resolution of 1920\u00d71080, with the distance between the person and the camera being greater than 2 meters. The cropped hand bounding boxes have an average resolution of less than 64\u00d764.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Comparison with SOTA<\/h2>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69f8bda90803a&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69f8bda90803a\" 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\/25CVPR-withSOTA.png\" 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\">Comparison with the state-of-the-art on the DexYCB dataset.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Bibtex<\/h2>\n\n\n\n<pre class=\"wp-block-code\"><code>@InProceedings{Fan_2025_CVPR,\n    author    = {Fan, Kaixin and Ren, Pengfei and Wang, Jingyu and Sun, Haifeng and Qi, Qi and Zhuang, Zirui and Liao, Jianxin},\n    title     = {Pose-Guided Temporal Enhancement for Robust Low-Resolution Hand Reconstruction},\n    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},\n    month     = {June},\n    year      = {2025},\n    pages     = {22627-22637}\n}<\/code><\/pre>\n<\/div>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>Kaixin Fan, Pengfei Ren*, Jingyu Wang\u2020<br \/>\n, Haifeng Sun, Qi Qi, Zirui Zhuang, Jianxin Liao<\/p>","protected":false},"author":1,"featured_media":2033,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[17],"tags":[],"class_list":["post-2031","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-rgb-based-3d-hand-pose-estimation"],"acf":{"writer":{"simple_value_formatted":"<code><em>This data type is not supported! 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