{"id":2,"date":"2026-04-20T03:32:34","date_gmt":"2026-04-20T03:32:34","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf3\/?page_id=2"},"modified":"2026-05-08T02:20:22","modified_gmt":"2026-05-08T02:20:22","slug":"sample-page","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf3\/","title":{"rendered":"Overview"},"content":{"rendered":"\n

Overview<\/strong><\/h2>\n\n\n\n

Motivation<\/strong><\/h3>\n\n\n\n

Generating animated 3D assets from monocular video is a fundamental challenge in computer vision with broad applications in AR\/VR, films, games, and simulations. Given only a monocular video as input, the goal is to produce a canonical mesh with per-frame deformations.<\/p>\n\n\n\n

Problem Statement<\/strong><\/h3>\n\n\n\n

Current methods face two major challenges:<\/strong><\/p>\n\n\n\n

Non-Rigging Methods:<\/strong> More freedom in animation, but struggle to handle occlusions and drastic topology or shape changes over time.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Rigging-Based Methods:<\/strong> Have difficulty keeping the skeleton and skinning stable when shape and appearance change non-rigidly. Do not support user-guided skeleton adjustment or editing-driven rig generation, making it difficult to refine or adapt the pipeline.<\/p>\n\n\n\n

Our Goal<\/strong><\/h3>\n\n\n\n

We propose two pipelines: a non-rigging pipeline that learns an optimal canonical mesh M* that correctly deforms at each timestep, and a rigging pipeline that enables interactive skeleton editing supporting MOVE, REMOVE, and ADD operations for skeleton-driven animation from monocular video.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Overview Motivation Generating animated 3D assets from monocular video is a fundamental challenge in computer vision with broad applications in AR\/VR, films, games, and simulations. Given only a monocular video as input, the goal is to produce a canonical mesh with per-frame deformations. Problem Statement Current methods face two major challenges: Non-Rigging Methods: More freedom […]<\/p>\n","protected":false},"author":264,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"yoast_head":"\nOverview - 3D Asset Generation: Generating Action Primitives<\/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\/2026teamf3\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Overview - 3D Asset Generation: Generating Action Primitives\" \/>\n<meta property=\"og:description\" content=\"Overview Motivation Generating animated 3D assets from monocular video is a fundamental challenge in computer vision with broad applications in AR\/VR, films, games, and simulations. 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