Tianye Li

I got my Ph.D. in Computer Science at USC, advised by Prof. Hao Li and Prof. Randall Hill, Jr. I also worked in the Vision and Graphics Lab at USC Institute for Creative Technologies. After graduation, I was a research scientist at Epic Games, Inc., working on digital humans for the next-generation games and AR/VR.

In summer and fall 2020, I was a research intern at the Facebook Reality Labs (Reality Labs, Meta), hosted by Zhaoyang Lv. In summer 2018, I was a research intern at Snap Inc., working with Chongyang Ma and Linjie Luo. From fall 2016 to spring 2017, I visited the Max Planck Institute for Intelligent Systems (Tübingen), working with Timo Bolkart, Javier Romero, and Michael J. Black.

I did my B.Eng. at Xidian University and M.Sc. (with honor) at USC, both in Electrical Engineering, during which I had worked in Agilent Technologies (Keysight) and Dolby Laboratories.

Email: <first_name> <last_name> at protonmail dot com

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My research interests are computer vision and computer graphics. Most of my work aim to capture and analyze the geometry, motion and appearance of dynamic and deformable objects, including human face and body as well as general objects and scenes.

TEMPEH reconstructs 3D heads in dense semantic correspondence directly from calibrated multi-view images. It is one step beyond ToFu, as it uses self-supervised training from scans to resolve ambiguous and imperfect dense correspondences, with head localization in a large capture volume and occlusion-aware feature fusion.

A method that captures complex dynamic scenes and enables photorealistic 3D video synthesis from wide view angles and at arbitary times.

We propose the ToFu framework that uses volumetric sampling to predict accurate base meshes in consistent topology directly from multi-view image inputs in only 0.385 seconds. ToFu also infers high-resolution skin appearance and detail maps, which enables photorealistic rendering.

An extended version of the Soft Rasterizer.

A rasterization-based differentiable renderer for 3D meshes, Soft Rasterizer (SoftRas), that supports reasoning for geometry, texture, lighting conditions and camera poses with 2D images. An extended version of SoftRas has been incorporated into the PyTorch3D library.

Given a face portrait, this system corrects the perspective distortion, easing subsequent facial recognition and reconstruction and reducing the bias for human perception.

Utilizing a learnt implicit representation for geometry, the method is able to capture dynamic performances of human actors with very sparse camera settings (3 or 4 views), which enables to high-quality volumetric videos.

We propose a light-weight yet expressive generic face model, FLAME, by learning from large high-quality datasets and an appropriate separation of identity, expression and pose. The FLAME model has been incorporated into the SMPL-X model.

A real-time facial performance capture system from single RGB camera, that is robust to occlusion, thanks to an effective and real-time facial segmentation network.

• CVPR (2020, 2021, 2022, 2023, 2024), ICCV (2019, 2021, 2023), ECCV (2020, 2022), SIGGRAPH (2022), SIGGRAPH Asia (2022, 2023), TPAMI (2021, 2022, 2023), IJCV (2020), NeurIPS (2020), AAAI (2020), VRST (2020), Eurographics (2019), Pacific Graphics (2018), CAVW (2018), ICCV Workshop PeopleCap (2017), IEEE VR (2017)

Latest update: January 1, 2024.
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