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István Sárándi

@Istvan_Sarandi • 1,187 subscribers

Postdoc @ Real Virtual Humans, Uni Tübingen, Germany @uni_tue. PhD from @RWTHVisionLab.

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I'll present #NLF, our SOTA 3D human pose and shape estimator at #NeurIPS2024 today: poster at East Exhibit Hall A-C #1304 from 11:00! Project: Models out for both PyTorch and TF! Further details to come soon!

I'll present #NLF, our SOTA 3D human pose and shape estimator at #NeurIPS2024 today: poster at East Exhibit Hall A-C #1304 from 11:00! Project: Models out for both PyTorch and TF! Further details to come soon!

István Sárándi

79,755 görüntüleme • 1 yıl önce

It's been a pleasure to work on this with Gerard Pons-Moll I think we found a really effective formulation for training large-scale strong pose and shape models. Here are some more qualitative results on tough, in-the-wild YouTube dance videos.

It's been a pleasure to work on this with Gerard Pons-Moll I think we found a really effective formulation for training large-scale strong pose and shape models. Here are some more qualitative results on tough, in-the-wild YouTube dance videos.

István Sárándi

40,556 görüntüleme • 2 yıl önce

Tomorrow I'll talk at the #ICRA workshop on "Enhancing human mobility" on recent 3D human estimation works from our group led by Gerard Pons-Moll. And I've released PyTorch training code for NLF, and more tools. Here's a preview of the even stronger upcoming DINOv2-ViTg-based NLF

Tomorrow I'll talk at the #ICRA workshop on "Enhancing human mobility" on recent 3D human estimation works from our group led by Gerard Pons-Moll. And I've released PyTorch training code for NLF, and more tools. Here's a preview of the even stronger upcoming DINOv2-ViTg-based NLF

István Sárándi

12,124 görüntüleme • 1 yıl önce

NLF is formulated in terms of an arbitrary keypoint estimation task, allowing to unify training data with different formats, with good pixel alignment. For SMPL output, we developed a super fast vertices-to-params fitter: And here's some more eye candy.

NLF is formulated in terms of an arbitrary keypoint estimation task, allowing to unify training data with different formats, with good pixel alignment. For SMPL output, we developed a super fast vertices-to-params fitter: And here's some more eye candy.

István Sárándi

10,162 görüntüleme • 1 yıl önce

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