Neng Qian

Currently I am a Graphic Engineer at NExT Studio where we are working to build photorealistic face model. Before, I was writing my master thesis at GVV group, MPI-INF, jointly supervised by Prof. Christian Theobalt and Prof. Bastian Leibe. I was a master student at RWTH Aachen, where I studied on computer vision, computer graphics and machine learning. I received my B.E. degree from Beijing Institute of Technology in 2017.

I'm interested in computer vision, computer graphics and the applications like AR/VR which combine both.

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Tracking and reconstructing the 3D pose and geometry of two hands in interaction is a challenging problem that has a high relevance for several human-computer interaction applications, including AR/VR, robotics, or sign language recognition. Existing works are either limited to simpler tracking settings (e.g., considering only a single hand or two spatially separated hands), or rely on less ubiquitous sensors, such as depth cameras. In contrast, in this work we present the first real-time method for motion capture of skeletal pose and 3D surface geometry of hands from a single RGB camera that explicitly considers close interactions. In order to address the inherent depth ambiguities in RGB data, we propose a novel multi-task CNN that regresses multiple complementary pieces of information, including segmentation, dense matchings to a 3D hand model, and 2D keypoint positions, together with newly proposed intra-hand relative depth and inter-hand distance maps. These predictions are subsequently used in a generative model fitting framework in order to estimate pose and shape parameters of a 3D hand model for both hands. We experimentally verify the individual components of our RGB two-hand tracking and 3D reconstruction pipeline through an extensive ablation study. Moreover, we demonstrate that our approach offers previously unseen two-hand tracking performance from RGB, and quantitatively and qualitatively outperforms existing RGB-based methods that were not explicitly designed for two-hand interactions. Moreover, our method even performs on-par with depth-based real-time methods.

We build the first parametric texture model of human hands. The model is registered to the popular MANO hand model. Furthermore, our model can be used to define a neural rendering layer that enables training with a self-upervised photometric loss. We make our model publicly available.

This project is for the fluid simulation lab course in 2018 SS at RWTH. It includes two particle-based fluid simulation solvers (PBF and WCSPH), and a visualization program based on Merely3D and imgui. It applys Marching cubes to reconstruct fluid surface.

An Orc warrior in World of Warcraft. Lok Tar Ogar!

Thanks to Jon Barron for the awesome website template.