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Meta presents Sapiens Foundation for Human Vision Models discuss: We present Sapiens, a family of models for four fundamental human-centric vision tasks - 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-resolution inference and are extremely easy to adapt for individual...

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AK

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151,511 görüntüleme • 1 yıl önce •via X (Twitter)

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The paper presents Sapiens, a family of vision transformer models trained on a large dataset of human images. The goal is to develop models that can generalize well, be applicable to a wide range of tasks, and produce high-quality outputs. The results demonstrate the benefit of pretraining on a large, curated dataset of human images. The models are able to generalize well to various scenarios, including multi-person scenes, egocentric views, and challenging poses. The high-resolution (1024x1024) pretraining and the detailed annotation of the finetuning datasets also contribute to the models' strong performance. full paper:

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Supreme1 yıl önce

normal map is mind blowing what the tech

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"Sapiens: Foundation for Human Vision Models" PAPER SUMMARY

Miguel Xochicale 🧑🏽‍🔬🤖〰️ profil fotoğrafı
Miguel Xochicale 🧑🏽‍🔬🤖〰️1 yıl önce

Nice one but these links are not working (will they open it soon?)

bryan pratte profil fotoğrafı
bryan pratte1 yıl önce

No code :(

Alessandro De Blasis profil fotoğrafı
Alessandro De Blasis1 yıl önce

Is it real-time or post-processing?

Alessandro De Blasis profil fotoğrafı
Alessandro De Blasis1 yıl önce

Want

Self-Attention Mechanism profil fotoğrafı
Self-Attention Mechanism1 yıl önce

can it spot a soldier and identify the head?

Cavit Erginsoy profil fotoğrafı
Cavit Erginsoy1 yıl önce

Why non commercial license @Meta 😵‍💫

Patryk Zoltowski profil fotoğrafı
Patryk Zoltowski1 yıl önce

Hope there will be some distilled model for realtime inference on mobile

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NVIDIA just announced EgoScale 🤖🧠 NVIDIA Research has uncovered a log-linear scaling law for robot dexterity by pretraining VLA models on over 20,000 hours of egocentric human video This massive dataset is 20 times larger than previous efforts and proves that robot intelligence follows a predictable path: the more human data, the lower the loss The secret is a simple recipe combining large-scale human pretraining with a small amount of aligned human-robot mid-training to bridge the gap In testing, this method boosted the average success rate by 54% on a 22-DoF robotic hand compared to policies built without pretraining EgoScale also enables one-shot task adaptation and works across different hardware, suggesting that human motion is a universal motor prior for robots Website: Paper: Source: NVIDIA Research #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #NVIDIA #EgoScale #GR00T

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