{"id":469,"date":"2023-12-18T23:54:24","date_gmt":"2023-12-18T23:54:24","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/?page_id=469"},"modified":"2023-12-19T04:50:16","modified_gmt":"2023-12-19T04:50:16","slug":"experiments-of-motiongpt-spring-2023","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/experiments-of-motiongpt-spring-2023\/","title":{"rendered":"Experiments of MotionGPT (Spring 2023)"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\" id=\"block-8427b7c9-a94e-47f3-bbf0-5f741978f199\"><strong>Dataset: <\/strong><\/h3>\n\n\n\n<p>During pretraining, we use &#8220;high-level&#8221; ground truth texts from the <strong>HumanML3D<\/strong> dataset and &#8220;low-level&#8221; ground truth text labels from the <strong>BABEL<\/strong> dataset.<\/p>\n\n\n\n<p><strong>HumanML3D<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"360\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/humanml3d-1024x360.png\" alt=\"\" class=\"wp-image-86\" style=\"width:538px;height:auto\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/humanml3d-1024x360.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/humanml3d-300x106.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/humanml3d-768x270.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/humanml3d-1536x540.png 1536w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/humanml3d-2048x720.png 2048w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Statistics: 14,616 motions and 44,970 descriptions composed by 5,371 distinct words. The total length of motions amounts to 28.59 hours, corresponding to the \u201c<strong>high-level<\/strong>\u201d conditioning in our model&nbsp;<\/li>\n<\/ul>\n\n\n\n<p><strong>BABEL<\/strong><\/p>\n\n\n\n<p><img decoding=\"async\" width=\"1280px;\" height=\"353px;\" src=\"https:\/\/lh7-us.googleusercontent.com\/AgmKUAhX4lSjEQxUaf54y7dvbMIB5pE9jPcaNAJ4tB5Re5QfTiX_QnvtlPURz69frw37KQbWQiV4ozDfJRw2aF5INyqp_4Gq2RsF9__-tG87XYN_SPxaaCiWy7Enl2BCZ_CMtUjVEAkeENk=s2048\"><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Contains 28k sequences with <strong>per-frame action label<\/strong>, for a total of 63k frame labels, corresponding to the \u201c<strong>low-level<\/strong>\u201d conditioning in our model&nbsp;<\/li>\n\n\n\n<li>Over 250 unique action categories<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Quantitative Result<\/strong><\/h3>\n\n\n\n<p><img decoding=\"async\" width=\"1923px;\" height=\"411px;\" src=\"https:\/\/lh7-us.googleusercontent.com\/0WPGrmetqO5xIFcBd-dvh6NyxmdxhR6YoHuOukaD4qUETmcoc67547ZJu__SG4D4mtjnZ-GGd5-QCp79Mj3prJMGcYS1FZqROWDmwHD_L9_4FAQdT81p6IZ2kUmA129FBdeYKG5TQMLbvqa3iWBJ52AserRS9QK2l9w=s2048\"><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Qualitative Result<\/strong><\/h3>\n\n\n\n<p><strong>Comparison with Baseline Model<\/strong><\/p>\n\n\n\n<p><strong>Text Prompt:&nbsp;<\/strong> <em>&nbsp;\u201ca man does a push-up and then uses his arms to balance himself back to his feet\u201d<\/em><\/p>\n\n\n\n<p><strong>Our Result:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"287\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/mdm_results_1-1024x287.png\" alt=\"\" class=\"wp-image-155\" style=\"width:688px;height:192px\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/mdm_results_1-1024x287.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/mdm_results_1-300x84.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/mdm_results_1-768x215.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/mdm_results_1.png 1446w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p><strong>Baseline\uff1a<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"208\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/ours_results_1-1024x208.png\" alt=\"\" class=\"wp-image-144\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/ours_results_1-1024x208.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/ours_results_1-300x61.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/ours_results_1-768x156.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/05\/ours_results_1.png 1416w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p>Here the text prompt is unseen in the training set, and has some complexity. It can be observed that  baseline method can not generate motion of &#8220;push-up&#8221; correctly. However, with  Large Language Model to further explain &#8220;push-up&#8221; and &#8220;balance himself&#8221; to the model, our  frameworks successfully generates the correct &#8220;push-up&#8221; motion and the subsequent motions. <\/p>\n\n\n\n<p><strong>Motion Synthesis with Same High-Level, Different Low-Level Text Prompts<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"364\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-15-1024x364.png\" alt=\"\" class=\"wp-image-504\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-15-1024x364.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-15-300x107.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-15-768x273.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-15.png 1487w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p><strong>Additional Qualitative Results<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1018\" height=\"992\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-14.png\" alt=\"\" class=\"wp-image-503\" style=\"width:416px;height:auto\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-14.png 1018w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-14-300x292.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-14-768x748.png 768w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>GPT-3 Prompting Examples<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"439\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-22-1024x439.png\" alt=\"\" class=\"wp-image-527\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-22-1024x439.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-22-300x129.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-22-768x330.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-22-1536x659.png 1536w, https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/wp-content\/uploads\/sites\/88\/2023\/12\/image-22.png 1734w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Dataset: During pretraining, we use &#8220;high-level&#8221; ground truth texts from the HumanML3D dataset and &#8220;low-level&#8221; ground truth text labels from the BABEL dataset. HumanML3D BABEL Quantitative Result Qualitative Result Comparison with Baseline Model Text Prompt:&nbsp; &nbsp;\u201ca man does a push-up and then uses his arms to balance himself back to his feet\u201d Our Result: Baseline\uff1a &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/experiments-of-motiongpt-spring-2023\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Experiments of MotionGPT (Spring 2023)&#8221;<\/span><\/a><\/p>\n","protected":false},"author":173,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-469","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Experiments of MotionGPT (Spring 2023) - Human Motion Synthesis<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team11\/experiments-of-motiongpt-spring-2023\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Experiments of MotionGPT (Spring 2023) - Human Motion Synthesis\" \/>\n<meta property=\"og:description\" content=\"Dataset: During pretraining, we use &#8220;high-level&#8221; ground truth texts from the HumanML3D dataset and &#8220;low-level&#8221; ground truth text labels from the BABEL dataset. 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