Meta Presents Sapiens: Foundation for Human Vision Models
Introduction
Large-scale pretraining followed by task-specific fine-tuning has transformed language modeling and is now revolutionizing computer vision. Notable models such as DINOv2, MAWS, and AIM have made significant strides in self-supervised feature generation and masked autoencoder scaling. However, existing methods often overlook human-centric approaches, focusing primarily on general image pretraining or zero-shot classification.
Practical Solutions and Value
This paper introduces Sapiens, a collection of high-resolution vision transformer models pretrained on millions of human images. Sapiens aims to advance the field of computer vision in areas such as 3D human digitization, keypoint estimation, and body-part segmentation, crucial for real-world applications.
The Sapiens models underwent comprehensive evaluation across four primary tasks: pose estimation, part segmentation, depth estimation, and normal estimation. Pretraining with the Human 300M dataset led to superior performance across all metrics. Performance was quantified using mAP for pose estimation, mIoU for segmentation, RMSE for depth estimation, and mean angular error for normal estimation.
In conclusion, Sapiens represents a significant advancement in human-centric vision models, demonstrating strong generalization across various tasks. Its exceptional performance stems from large-scale pretraining on a curated dataset, high-resolution vision transformers, and high-quality annotations. Positioned as a foundational element for downstream tasks, Sapiens makes high-quality vision backbones more accessible.
AI Solutions for Business
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