[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-new-model-predicts-how-3d-objects-move-from-few-scans":10,"sections":35},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},6927,"new-model-predicts-how-3d-objects-move-from-few-scans","New Model Predicts How 3D Objects Move From Few Scans","A new feed-forward AI model called FAMOS predicts which parts of an object move and how, using just a handful of partial 3D scans.","A new AI system called FAMOS can figure out how a chair, drawer, or robot arm moves after seeing just a few partial 3D scans of it.\n\nResearchers built FAMOS, a feed-forward model that predicts which parts of an object are movable and estimates their joint parameters - the hinges, slides, and rotations that define how something like a laptop hinge or cabinet door actually works. Unlike most prior systems, which infer motion from a single snapshot and lean heavily on learned shape assumptions, FAMOS can take in any number of sparse, unordered point-cloud observations, including just one, and reason across all of them at once. It does this with what the team calls a Multi-state Articulation Transformer, which alternates attention within each observation and across the full set. The researchers also trained it with a new objective that tracks the full range of motion a part shows across observations, plus a procedural data generator that creates its own labeled training assets to work around the shortage of existing articulated-object datasets.\n\nRobots and AR systems increasingly need to predict not just what an object looks like, but how its parts move, and doing that from a handful of quick scans - rather than a full 3D scan or CAD model - is the practical bottleneck. FAMOS reportedly beat both other feed-forward models and slower optimization-based methods on three benchmark datasets, PartNet-Mobility, ACD, and ArtiCraft-10K.\n\nIt's a narrow, technical improvement, not a flashy demo - but stitching together partial views instead of guessing from a single glance is exactly the kind of unglamorous fix that tends to compound in robotics pipelines.","[\"computer vision\",\"3d modeling\",\"robotics\",\"ai research\"]","2026-09-18T04:00:00.000Z","2026-09-18T23:01:11.547Z","2026-09-18T23:01:23.472Z","published",null,[],"ai",[26,27,28,29],"computer vision","3d modeling","robotics","ai research",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.20817",0,{"sections":36},[37,40,44,49,54,58,62,67,71,76,81,86,91,96],{"name":38,"slug":24,"count":39,"latest_published_at":18},"AI",4082,{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",661,{"name":45,"slug":46,"count":47,"latest_published_at":48},"Policy","policy",339,"2026-09-17T12:00:00.000Z",{"name":50,"slug":51,"count":52,"latest_published_at":53},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":18},"Hardware","hardware",155,{"name":59,"slug":60,"count":61,"latest_published_at":18},"Science","science",125,{"name":63,"slug":64,"count":65,"latest_published_at":66},"Consumer Tech","consumer-tech",99,"2026-09-09T17:27:33.000Z",{"name":68,"slug":69,"count":70,"latest_published_at":18},"Dev Tools","dev-tools",78,{"name":72,"slug":73,"count":74,"latest_published_at":75},"Software","software",75,"2026-09-10T20:41:21.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Startups","startups",55,"2026-09-09T23:14:29.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":85},"Gaming","gaming",43,"2026-09-10T12:18:06.000Z",{"name":87,"slug":88,"count":89,"latest_published_at":90},"General","general",41,"2026-09-08T01:57:23.000Z",{"name":92,"slug":93,"count":94,"latest_published_at":95},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":97,"slug":98,"count":99,"latest_published_at":100},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]