I am a fourth-year Ph.D. student in the Stanford Computational Imaging Laboratory, advised by Prof. Gordon Wetzstein. My research interest lies in neural scene representations - the way neural networks learn to represent information on our 3D world. My goal is to allow independent agents to reason about our world given visual observations, such as inferring a complete model of a scene with information on geometry, material, lighting etc. from only few observations, a task that is simple for humans, but currently impossible for AI. I have previously worked on differentiable camera pipelines, VR and human perception.
Our state-of-the-art report on neural rendering was accepted to Eurographics 2020. Drop by our tutorial!
Our paper "Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations" wins an honorable mention for "Outstanding New Directions" at NeurIPS 2019! Watch my talk here
I will join Prof. Noah Snavely's group at the Google NYC office over the summer and continue working
on deep learning for scene understanding and novel view synthesis.
Our paper "DeepVoxels: Learning Persistent 3D Feature Embeddings" was accepted to CVPR as an oral!
I will be in Los Angeles from June 16 to June 21 to present the paper.
State of the Art on Neural Rendering
Computer Graphics Forum 2020 - EG 2020 (STAR Report)
Inferring Semantic Information with3D Neural Scene Representations
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
NeurIPS 2019 (Oral, Honorable Mention "Outstanding New Directions")
DeepVoxels: Learning Persistent 3D Feature Embeddings
CVPR 2019 (Oral)
Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification
End-to-end Optimization of Optics and Image Processing for Achromatic Extended Depth of Field and Super-resolution Imaging
Saliency in VR: How do people explore virtual environments?
IEEE VR 2018
Movie Editing and Cognitive Event Segmentation in Virtual Reality Video
Towards a Machine-learning Approach for Sickness Prediction in 360° Stereoscopic Videos
IEEE VR 2018