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HMD-NeMo - Sadegh Aliakbarian

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Contenu fourni par Itzik Ben-Shabat. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Itzik Ben-Shabat ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

🎙️Join us on this exciting episode of the Talking Papers Podcast as we sit down with the talented Sadegh Aliakbarian to explore his groundbreaking ICCV 2023 paper "HMD-NeMo: Online 3D Avatar Motion Generation From Sparse Observations" . Our guest, will take us on a journey through this pivotal research that addresses a crucial aspect of immersive mixed reality experiences.
🌟 The quality of these experiences hinges on generating plausible and precise full-body avatar motion, a challenge given the limited input signals provided by Head-Mounted Devices (HMDs), typically head and hands 6-DoF. While recent approaches have made strides in generating full-body motion from such inputs, they assume full hand visibility. This assumption, however, doesn't hold in scenarios without motion controllers, relying instead on egocentric hand tracking, which can lead to partial hand visibility due to the HMD's field of view.
🧠 "HMD-NeMo" presents a groundbreaking solution, offering a unified approach to generating realistic full-body motion even when hands are only partially visible. This lightweight neural network operates in real-time, incorporating a spatio-temporal encoder with adaptable mask tokens, ensuring plausible motion in the absence of complete hand observations.
👤 Sadegh is currently a senior research scientist at Microsoft Mixed Reality and AI Lab-Cambridge (UK), where he's at the forefront of Microsoft Mesh and avatar motion generation. He holds a PhD from the Australian National University, where he specialized in generative modeling of human motion. His research journey includes internships at Amazon AI, Five AI, and Qualcomm AI Research, focusing on generative models, representation learning, and adversarial examples.
🤝 We first crossed paths during our time at the Australian Centre for Robotic Vision (ACRV), where Sadegh was pursuing his PhD, and I was embarking on my postdoctoral journey. During this time, I had the privilege of collaborating with another co-author of the paper, Fatemeh Saleh, who also happens to be Sadegh's life partner. It's been incredible to witness their continued growth.
🚀 Join us as we uncover the critical advancements brought by "HMD-NeMo" and their implications for the future of mixed reality experiences. Stay tuned for the episode release!
All links and resources are available in the blogpost: https://www.itzikbs.com/hmdnemo

🎧Subscribe on your favourite podcast app: https://talking.papers.podcast.itzikbs.com

📧Subscribe to our mailing list: http://eepurl.com/hRznqb

🐦Follow us on Twitter: https://twitter.com/talking_papers

🎥YouTube Channel: https://bit.ly/3eQOgwP

  continue reading

33 episodes

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HMD-NeMo - Sadegh Aliakbarian

Talking Papers Podcast

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Manage episode 378213782 series 3300270
Contenu fourni par Itzik Ben-Shabat. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Itzik Ben-Shabat ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

🎙️Join us on this exciting episode of the Talking Papers Podcast as we sit down with the talented Sadegh Aliakbarian to explore his groundbreaking ICCV 2023 paper "HMD-NeMo: Online 3D Avatar Motion Generation From Sparse Observations" . Our guest, will take us on a journey through this pivotal research that addresses a crucial aspect of immersive mixed reality experiences.
🌟 The quality of these experiences hinges on generating plausible and precise full-body avatar motion, a challenge given the limited input signals provided by Head-Mounted Devices (HMDs), typically head and hands 6-DoF. While recent approaches have made strides in generating full-body motion from such inputs, they assume full hand visibility. This assumption, however, doesn't hold in scenarios without motion controllers, relying instead on egocentric hand tracking, which can lead to partial hand visibility due to the HMD's field of view.
🧠 "HMD-NeMo" presents a groundbreaking solution, offering a unified approach to generating realistic full-body motion even when hands are only partially visible. This lightweight neural network operates in real-time, incorporating a spatio-temporal encoder with adaptable mask tokens, ensuring plausible motion in the absence of complete hand observations.
👤 Sadegh is currently a senior research scientist at Microsoft Mixed Reality and AI Lab-Cambridge (UK), where he's at the forefront of Microsoft Mesh and avatar motion generation. He holds a PhD from the Australian National University, where he specialized in generative modeling of human motion. His research journey includes internships at Amazon AI, Five AI, and Qualcomm AI Research, focusing on generative models, representation learning, and adversarial examples.
🤝 We first crossed paths during our time at the Australian Centre for Robotic Vision (ACRV), where Sadegh was pursuing his PhD, and I was embarking on my postdoctoral journey. During this time, I had the privilege of collaborating with another co-author of the paper, Fatemeh Saleh, who also happens to be Sadegh's life partner. It's been incredible to witness their continued growth.
🚀 Join us as we uncover the critical advancements brought by "HMD-NeMo" and their implications for the future of mixed reality experiences. Stay tuned for the episode release!
All links and resources are available in the blogpost: https://www.itzikbs.com/hmdnemo

🎧Subscribe on your favourite podcast app: https://talking.papers.podcast.itzikbs.com

📧Subscribe to our mailing list: http://eepurl.com/hRznqb

🐦Follow us on Twitter: https://twitter.com/talking_papers

🎥YouTube Channel: https://bit.ly/3eQOgwP

  continue reading

33 episodes

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