Zerong Zheng

We introduce a robust template-less human volumetric capture system combined with various data-driven visual cues, which outperforms existing state-of-the-art approaches significantly.

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We propose an efficient method for robust 3d self-portraits using a single RGBD camera. Our method can generate detailed 3D self-portraits in seconds and is able to handle challenging clothing topologies.

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We propose DeepHuman, a deep learning based framework for 3D human reconstruction from a single RGB image. We also contribute THuman, a 3D real-world human model dataset containing approximately 7000 models.

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This paper proposes a new method for live free-viewpoint human performance capture with dynamic details (e.g., cloth wrinkles) using a single RGBD camera. By incorporating cloth simulation into the performance capture pipeline, we can generate plausible cloth dynamics and cloth-body interactions.

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We propose a light-weight and highly robust real-time human performance capture method based on a single depth camera and sparse inertial measurement units (IMUs).

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We propose DoubleFusion, a new real-time system that combines volumetric dynamic reconstruction with datadriven template fitting to simultaneously reconstruct detailed geometry, non-rigid motion and the inner human body shape from a single depth camera.

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