BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Handformer2T: A Lightweight Regression-based model for Interac ting Hands Pose Estimation from a single RGB Image X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2023-12-04-deying-kong@cml.ics.uci.edu DTSTAMP:20231204T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20231204T130000 DTEND;TZID=America/Los_Angeles:20231204T140000 SUMMARY:[CML Seminar] Deying Kong: Handformer2T: A Lightweight Regression-b ased model for Interacting Hands Pose Estimation from a single RGB Image LOCATION:Donald Bren Hall 4011 DESCRIPTION:Deying Kong\, Software Engineer\, Google\n\nTitle: Handformer2T : A Lightweight Regression-based model for Interacting Hands Pose Estimati on from a single RGB Image\n\nAbstract: Despite its extensive range of pot ential applications in virtual reality and augmented reality\, 3D interact ing hand pose estimation from an RGB image remains a very challenging prob lem\, due to appearance confusions between keypoints of the two hands\, an d severe hand-hand occlusion. Due to their ability to capture long range r elationships between keypoints\, transformer-based methods have gained pop ularity. However\, existing methods usually deploy tokens at keypoint leve l\, which results in high computational and memory complexity. In this tal k\, we propose a novel mechanism\, hand-level tokenization\, where we depl oy only one token for each hand. We also propose a pose query enhancer mod ule\, which refines the pose prediction iteratively. As a result\, our pro posed model\, Handformer2T\, can achieve high performance while remaining lightweight.\n\nhttps://cml.ics.uci.edu/seminars/2023-12-04-deying-kong X-ALT-DESC;FMTTYPE=text/html:Deying Kong\, Software Engi neer\, Google

Title: Handformer2T: A Lightweight Regression- based model for Interacting Hands Pose Estimation from a single RGB Image< br>
Abstract: Despite its extensive range of potential applicati ons in virtual reality and augmented reality\, 3D interacting hand pose es timation from an RGB image remains a very challenging problem\, due to app earance confusions between keypoints of the two hands\, and severe hand-ha nd occlusion. Due to their ability to capture long range relationships bet ween keypoints\, transformer-based methods have gained popularity. However \, existing methods usually deploy tokens at keypoint level\, which result s in high computational and memory complexity. In this talk\, we propose a novel mechanism\, hand-level tokenization\, where we deploy only one toke n for each hand. We also propose a pose query enhancer module\, which refi nes the pose prediction iteratively. As a result\, our proposed model\, Ha ndformer2T\, can achieve high performance while remaining lightweight.

http s://cml.ics.uci.edu/seminars/2023-12-04-deying-kong URL:https://cml.ics.uci.edu/seminars/2023-12-04-deying-kong END:VEVENT END:VCALENDAR