BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260318T173107Z UID:59F9CCA8-A93B-499F-8DE5-B0C9E261A8CF DTSTART;TZID=America/New_York:20260327T100000 DTEND;TZID=America/New_York:20260327T233000 DESCRIPTION:Free Visitor Parking is on the 4th Level of the Parking garage. \n\nAbstract: In recent years\, 3D human activity recognition and tracking has become an important topic in human-computer interaction. To preserve the privacy of users\, there is considerable interest in techniques withou t using a video camera. In this talk\, we first present RFID-Pose\, a visi on-assisted 3D human pose estimation system based on deep learning (DL). T he performance of DL models depends on the availability of sufficient high -quality radio frequency (RF) data\, which is more difficult and expensive to collect than other types of data. To overcome this obstacle\, in the s econd part of this talk\, we present generative AI approaches to generate labeled synthetic RF data for multiple wireless sensing platforms\, such a s WiFi\, RFID\, and mmWave radar\, including a conditional Recurrent Gener ative Adversarial Network (R-GAN) approach and diffusion/latent diffusion based approaches. Next\, we propose a novel framework that leverages laten t diffusion transformers to synthesize high quality RF data\, as well as a latent diffusion transformer with cross-attention conditioning to accurat ely infer missing joints in skeletal poses\, completing full 25-joint conf igurations from partial (i.e.\, 12-joint) inputs utilizing received RF sen sory data. Finally\, we present our recent work TF-Diff\, a novel training -free diffusion framework for cross-domain radio frequency (RF)-based huma n activity recognition (HAR) system\, which enables effective adaptation w ith minimal target-domain data.\n\nSpeaker(s): Shiwen Mao\, \n\nRoom: 119c \, Bldg: GTRI Conference Center Room 119C\, 250 14th St NW\, \, Atlanta\, \, Georgia\, United States\, 30318 LOCATION:Room: 119c\, Bldg: GTRI Conference Center Room 119C\, 250 14th St NW\, \, Atlanta\, \, Georgia\, United States\, 30318 ORGANIZER:Bpage1@ieee.org SEQUENCE:45 SUMMARY:Diffusion-enabled 3D Human Pose Tracking\, Data Augmentation\, Comp letion\, and Acceleration URL;VALUE=URI:https://events.vtools.ieee.org/m/549536 X-ALT-DESC:Description: <br /><p>&nbsp\;</p>\n<p>Free Visitor Parking is on the 4th Level of the Parking garage.&nbsp\;</p>\n<p>&nbsp\;</p>\n<p>Abstr act: In recent years\, 3D human activity recognition and tracking has beco me an important topic in human-computer interaction. To preserve the priva cy of users\, there is considerable interest in techniques without using a video camera. In this talk\, we first present RFID-Pose\, a vision-assist ed 3D human pose estimation system based on deep learning (DL). The perfor mance of DL models depends on the availability of sufficient high-quality radio frequency (RF) data\, which is more difficult and expensive to colle ct than other types of data. To overcome this obstacle\, in the second par t of this talk\, we present generative AI approaches to generate labeled s ynthetic RF data for multiple wireless sensing platforms\, such as WiFi\, RFID\, and mmWave radar\, including a conditional Recurrent Generative Adv ersarial Network (R-GAN) approach and diffusion/latent diffusion based app roaches. Next\, we propose a novel framework that leverages latent diffusi on transformers to synthesize high quality RF data\, as well as a latent d iffusion transformer with cross-attention conditioning to accurately infer missing joints in skeletal poses\, completing full 25-joint configuration s from partial (i.e.\, 12-joint) inputs utilizing received RF sensory data . Finally\, we present our recent work TF-Diff\, a novel training-free dif fusion framework for cross-domain radio frequency (RF)-based human activit y recognition (HAR) system\, which enables effective adaptation with minim al target-domain data.&nbsp\;</p> END:VEVENT END:VCALENDAR