Sapiens2
This work advances human-centric computer vision by providing a versatile, high-fidelity model family that sets new benchmarks across multiple dense prediction tasks, benefiting applications in graphics, AR/VR, and human analysis.
Sapiens2 introduces a family of high-resolution transformers (0.4B to 5B parameters) for human-centric vision, achieving new state-of-the-art results across pose estimation (+4 mAP), body-part segmentation (+24.3 mIoU), and normal estimation (45.6% lower angular error) through a unified pretraining objective combining masked image reconstruction and self-distilled contrastive learning on 1 billion human images.
We present Sapiens2, a model family of high-resolution transformers for human-centric vision focused on generalization, versatility, and high-fidelity outputs. Our model sizes range from 0.4 to 5 billion parameters, with native 1K resolution and hierarchical variants that support 4K. Sapiens2 substantially improves over its predecessor in both pretraining and post-training. First, to learn features that capture low-level details (for dense prediction) and high-level semantics (for zero-shot or few-label settings), we combine masked image reconstruction with self-distilled contrastive objectives. Our evaluations show that this unified pretraining objective is better suited for a wider range of downstream tasks. Second, along the data axis, we pretrain on a curated dataset of 1 billion high-quality human images and improve the quality and quantity of task annotations. Third, architecturally, we incorporate advances from frontier models that enable longer training schedules with improved stability. Our 4K models adopt windowed attention to reason over longer spatial context and are pretrained with 2K output resolution. Sapiens2 sets a new state-of-the-art and improves over the first generation on pose (+4 mAP), body-part segmentation (+24.3 mIoU), normal estimation (45.6% lower angular error) and extends to new tasks such as pointmap and albedo estimation. Code: https://github.com/facebookresearch/sapiens2