Research
I'm interested in machine learning and computer vision, especially visual intelligence and representing the 3D world from one or few images.
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Real-Time Radiance Fields for Single-Image Portrait View Synthesis
Alex Trevithick,
Matthew Chan,
Michael Stengel,
Eric R. Chan,
Chao Liu,
Zhiding Yu,
Sameh Khamis,
Manmohan Chandraker,
Ravi Ramamoorthi,
Koki Nagano
ACM Transactions on Graphics (SIGGRAPH), 2023
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paper
Real-time encoding and view synthesis from a single portrait image learned with strictly synthetic data.
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NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion
Jiatao Gu,
Alex Trevithick,
Kai-En Lin,
Josh Susskind,
Christian Theobalt,
Lingjie Liu,
Ravi Ramamoorthi
International Conference on Machine Learning (ICML), 2023
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paper /
code
Distill the knowledge of a 3D-aware conditional diffusion model into a triplane NeRF.
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GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering
Alex Trevithick,
Bo Yang
International Conference on Computer Vision (ICCV), 2021
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video
Per-pixel features improve NeRF and allow it to generalize to new scenes without retraining.
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Updates
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6/19/23: Starting second internship at NVIDIA AI.
6/8/23: Giving talk at the annual UCSD visual computing retreat.
5/29/23: Jensen presents our work at Computex!
5/26/23: Giving talk at Google Labs on Live 3D Portrait.
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