We release InstantHMR, an open-source model for real-time monocular 3D human pose estimation and mesh recovery! The model runs at ~200 FPS on an RTX 4070 and achieves ~120 FPS on a Samsung Galaxy S23)
🤗 Hugging Face:https://t.co/4py8wY6PQw
💻 GitHub: https://t.co/Zz7no1CUeB
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Current 3D human pose reconstruction models are very impressive, but which model should you pick for your application? Introducing the Caltech Tennis Dataset (CalTennis), a large-scale video benchmark for evaluating monocular-to-3D pose estimation in the wild. CalTennis comprises over 11 million frames (51 hours) of tennis practice and match play. It is 10× larger than existing in-the-wild human motion video datasets and 3× larger than existing MOCAPground-truthed datasets, and it is the first large-scale benchmark to provide synchronized multi-view recordings of expert athletic motion.
@KlawGhost Non en vrai pour moi y’a rouge les 3 aussi frr. Je voulais dire que comme ils ont pas mis rouge pour les autre faut pas mettre là nn plus quoi
Why diffusion denoising-based generative methods do not suffer the curse of dimensionality even though the data may lie in extremely high-dim spaces? Our new work, accepted by the JMLR: https://t.co/njMEqzH3TF reveals the not-so-surprising secret: as long as the intrinsic dimension of the distribution is very low, the generative process can be extremely efficient and effective! It seems that a mixture of low-rank Gaussians is a universal model for all informative real-world data. as we stipulated in a former textbook of mine: Generalized Principal Component Analysis: https://t.co/nEy8qcFN7e, published exactly ten years ago!
@Squadra213@okbadu13009@ArchivoVAR Tu dis de la merde, tu vois sur cette image que maza n’a pas encore touché le ballon et chaibi etait deja hors jeu. Arrete de nous afficher pr rien