Update README.md

README.md

@@ -1,5 +1,30 @@
----
-license: other
-license_name: s-lab-license
-license_link: LICENSE
----
+---
+license: other
+license_name: s-lab-license
+license_link: LICENSE
+language:
+- en
+tags:
+- Synthetic Data
+- 3D Humans
+- Human Pose and Shape Estimation
+- Human NeRF
+pretty_name: SynBody
+size_categories:
+- 100B<n<1T
+---
+
+# SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling
+
+[Homepage](https://synbody.github.io/)
+
+Abstract: Synthetic data has emerged as a promising source for 3D human research as it offers low-cost access to large-scale human datasets. 
+To advance the diversity and annotation quality of human models, we introduce a new synthetic dataset, SynBody, with three appealing features: 
+1) a clothed parametric human model that can generate a diverse range of subjects;
+2) the layered human representation that naturally offers high-quality 3D annotations to support multiple tasks;
+3) a scalable system for producing realistic data to facilitate real-world tasks.
+
+The dataset comprises 1.2M images with corresponding accurate 3D annotations, covering 10,000 human body models, 1,187 actions, and various viewpoints.
+The dataset includes two subsets for human pose and shape estimation as well as human neural rendering. Extensive experiments on SynBody indicate that
+it substantially enhances both SMPL and SMPL-X estimation. Furthermore, the incorporation of layered annotations offers a valuable training resource
+for investigating the Human Neural Radiance Fields (NeRF).