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README.md

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----
-license: other
-license_name: s-lab-license
-license_link: LICENSE
----
+---
+license: other
+license_name: s-lab-license
+license_link: LICENSE
+language:
+- en
+tags:
+- Human Pose and Shape Estimation
+- 3D Human Recovery
+- Parametric Humans
+- Synthetic Data
+pretty_name: GTA-Human
+size_categories:
+- 100B<n<1T
+---
+
+Please visit our [Homepage](https://caizhongang.com/projects/GTA-Human/) and [Toolbox](https://github.com/caizhongang/gta-human_toolbox/tree/main) for more information.
+
+Abstract: 
+Image- and video-based 3D human recovery ( i.e. , pose and shape estimation) have achieved substantial progress. 
+However, due to the prohibitive cost of motion capture, existing datasets are often limited in scale and diversity. 
+In this work, we obtain massive human sequences by playing the video game with automatically annotated 3D ground truths. 
+Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine, featuring 
+a highly diverse set of subjects, actions, and scenarios. More importantly, we study the use of game-playing data 
+and obtain five major insights. First , game-playing data is surprisingly effective. A simple frame-based baseline 
+trained on GTA-Human outperforms more sophisticated methods by a large margin. For videobased methods, GTA-Human 
+is even on par with the in-domain training set. Second , we discover that synthetic data provides critical complements 
+to the real data that is typically collected indoor. We highlight that our investigation into domain gap provides 
+explanations for our data mixture strategies that are simple yet useful, which offers new insights to the research 
+community. Third , the scale of the dataset matters. The performance boost is closely related to the additional 
+data available. A systematic study on multiple key factors (such as camera angle and body pose) reveals that the 
+model performance is sensitive to data density. Fourth , the effectiveness of GTA-Human is also attributed to the 
+rich collection of strong supervision labels (SMPL parameters), which are otherwise expensive to acquire in real 
+datasets. Fifth , the benefits of synthetic data extend to larger models such as deeper convolutional neural networks 
+(CNNs) and Transformers, for which a significant impact is also observed. We hope our work could pave the way for 
+scaling up 3D human recovery to the real world.
+