Tianye Li
I'm a research scientist in the AI-Mediated Reality and Interaction (AMRI) group at NVIDIA Research, working on digital humans, generative models and dynamic reconstruction (e.g., volumetric videos). Prior to that, I was a research scientist at Epic Games, Inc., working with Christoph Lassner and Iain Matthews.
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.
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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Research
My research interests are computer vision and computer graphics. Most of my work aims to capturing, modeling and understanding our dynamic 3D world. This involves analyzing the geometry, motion and appearance for dynamic humans, objects and scenes.
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GAIA: Generative Animatable Interactive Avatars with Expression-conditioned Gaussians
Zhengming Yu,
Tianye Li,
Jingxiang Sun,
Omer Shapira,
Seonwook Park,
Michael Stengel
Matthew Chan,
Xin Li,
Wenping Wang,
Koki Nagano,
Shalini De Mello
SIGGRAPH 2025
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GAIA generates animation-ready Gaussian avatars by learning only on in-the-wild image datasets. GAIA supports photorealistic novel view synthesis, individual control of identity and expression, and interactive animation and editing.
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BLADE: Single-view Body Mesh Estimation through Accurate Depth Estimation
Shengze Wang,
Jiefeng Li,
Tianye Li,
Ye Yuan,
Henry Fuchs,
Koki Nagano,
Shalini De Mello,
Michael Stengel
CVPR 2025
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BLADE is a human mesh recovery method that accurately recovers perspective parameters from a single image. BLADE outperforms existing methods at estimating subject depth, focal parameters, 3D pose, and 2D alignment.
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QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint Videos
Sharath Girish,
Tianye Li*,
Amrita Mazumdar*,
Abhinav Shrivatava,
David Luebke,
Shalini De Mello
NeurIPS 2024
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We develop efficient representations for streamable free-viewpoint videos with dynamic Gaussians. QUEEN is able to capture dynamic scenes at high visual quality and reduce the model size to just 0.7 MB per frame while training in under 5 sec and rendering at ∼350 FPS. QUEEN is featured at GTC 2025 (San Jose and Paris).
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Instant Multi-View Head Capture through Learnable Registration
Timo Bolkart,
Tianye Li,
Michael J. Black
CVPR 2023
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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.
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Neural 3D Video Synthesis from Multi-view Video
Tianye Li*,
Mira Slavcheva*,
Michael Zollhoefer,
Simon Green,
Christoph Lassner,
Changil Kim,
Tanner Schmidt,
Steven Lovegrove,
Michael Goesele,
Richard Newcombe,
Zhaoyang Lv
CVPR 2022 (Oral Presentation)
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We propose a novel and compact dynamic neural radiance field (DyNeRF) that captures complex dynamic scenes and enables photorealistic 3D video synthesis from wide view angles and at arbitary times. We also present the neural 3D video synthesis dataset.
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Topologically Consistent Multi-View Face Inference Using Volumetric Sampling
Tianye Li,
Shichen Liu,
Timo Bolkart,
Jiayi Liu,
Hao Li,
Yajie Zhao
ICCV 2021 (Oral Presentation)
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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.
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A General Differentiable Mesh Renderer for Image-based 3D Reasoning
Shichen Liu,
Tianye Li,
Weikai Chen,
Hao Li
TPAMI 2020
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An extended version of the Soft Rasterizer.
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Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
Shichen Liu,
Tianye Li,
Weikai Chen,
Hao Li
ICCV 2019 (Oral Presentation)
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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.
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Learning Perspective Undistortion of Portraits
Yajie Zhao,
Zeng Huang,
Tianye Li,
Weikai Chen,
Chloe LeGendre,
Xinglei Ren,
Jun Xing,
Ari Shapiro,
Hao Li
ICCV 2019 (Oral Presentation)
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Given a face portrait, this system corrects the perspective distortion, easing subsequent facial recognition and reconstruction and reducing the bias for human perception.
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Deep Volumetric Video from Very Sparse Multi-view Performance Capture
Zeng Huang,
Tianye Li,
Weikai Chen,
Yajie Zhao,
Jun Xing,
Chloe LeGendre,
Linjie Luo,
Chongyang Ma,
Hao Li
ECCV 2018
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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.
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Learning a Model of Facial Shape and Expression from 4D Scans
Tianye Li*,
Timo Bolkart*,
Michael J. Black,
Hao Li,
Javier Romero
SIGGRAPH Asia 2017
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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.
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Real-Time Facial Segmentation and Performance Capture from RGB Input
Shunsuke Saito,
Tianye Li,
Hao Li
ECCV 2016
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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.
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Democratizing Immersive Experiences with NVIDIA AI
Amrita Mazumdar*,
Tianye Li*,
Michael Stengel,
Jonghyun Kim,
Shalini De Mello
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Wil Braithwaite,
Cheng Sun,
Seonwook Park
GTC 2025 San Jose & Paris
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We present novel immersive 3D experiences that allows users to move around in a streaming volumetric video, in 3D, in real-time. This allows for a highly immersive video viewing experience, especially when paired with 3D displays such as light field display or virtual/mixed reality headsets.
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Organizer:
CVPR 2025 Tutorial on Volumetric Video in the Real World
CVPR 2025 Workshop on Photorealistic 3D Head Avatars (P3HA)
Reviewer:
CVPR (2020-2025), ICCV (2019-2023), ECCV (2020-2024), SIGGRAPH (2022, 2024, 2025), SIGGRAPH Asia (2022-2025), TPAMI (2021-2024), IJCV (2020), CVIU (2024), NeurIPS (2020, 2024, 2025), AAAI (2020), VRST (2020), Eurographics (2019), Pacific Graphics (2018), CAVW (2018), ICCV Workshop PeopleCap (2017), IEEE VR (2017)
Outstanding Reviewer, CVPR 2020
Outstanding Reviewer, CVPR 2021
Outstanding Reviewer, ICCV 2021 ("top" reviewers in computer vision 2020-2021)
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Teaching Assistant, CSCI 576 Multimedia Systems Design, Fall 2021
Teaching Assistant, CSCI 677 Advanced Computer Vision , Fall 2019
Teaching Assistant, CSCI 621 Digital Geometry Processing, Spring 2018
Grader, EE 559 Mathematical Pattern Recognition, Spring 2015
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Latest update: July 20, 2025.
Wonderful template from Jon Barron.
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