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README.md

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-# Human101
-The official implementation of "Human101: Training 100+FPS Human Gaussians in 100s from 1 View"
+# Human101: Training 100+FPS Human Gaussians in 100s from 1 View
+
+This is the official implementation of "Human101: Training 100+FPS Human Gaussians in 100s from 1 View".
+![pipeline](./assets/pipeline.png)
+
+## Abstract
+
+Reconstructing the human body from single-view videos plays a pivotal role in the virtual reality domain. One prevalent application scenario necessitates the rapid reconstruction of high-fidelity 3D digital humans while simultaneously ensuring real-time rendering and interaction. Existing methods often struggle to fulfill both requirements. In this paper, we introduce Human101, a novel framework adept at producing high-fidelity dynamic 3D human reconstructions from 1-view videos by training 3D Gaussians in 100 seconds and rendering in 100+ FPS. Our method leverages the strengths of 3D Gaussian Splatting, which provides an explicit and efficient representation of 3D humans. Standing apart from prior NeRF-based systems, Human101 ingeniously applies a Human-centric Forward Gaussian Animation to deform the parameters of 3D Gaussians, thereby enhancing rendering speed (i.e., rendering 1024-resolution images at an impressive 60+ FPS and rendering 512-resolution images at 100+ FPS). Experimental results indicate that our approach substantially eclipses current methods, clocking up to a 10 $ \times $ surge in frames per second and delivering comparable or superior rendering quality.
+
+<!-- # News -->
+
+## TODO list
+
+- [ ] Release demos & project page
+- [ ] Release code
+
+## Acknowledgement
+
+Our implementation is mainly based on [3D Gaussian Splatting](https://github.com/graphdeco-inria/gaussian-splatting) , [Instant-nvr](https://github.com/zju3dv/instant-nvr) and [InstantAvatar](https://github.com/tijiang13/InstantAvatar)
+and many thanks to the following open-source projects:
+
+- [ECON](https://github.com/YuliangXiu/ECON)
+- [GTA](https://github.com/River-Zhang/GTA)
+- [EasyMoCap](https://github.com/zju3dv/EasyMocap/)
+
+And many thanks to the authors of [GTA](https://github.com/River-Zhang/GTA) and [TransHuman](https://github.com/pansanity666/TransHuman) for discussing some details about the implementation.
+
+More related papers about 3D avatars: [Awesome-3D-Avatars](https://github.com/pansanity666/Awesome-Avatars)