Biography
[more]
Tiange Xiang
(向天戈)
is a forth-year CS Ph.D. student at Stanford University and a student researcher at Google DeepMind. He is affiliated with Stanford AI Lab & Stanford Vision and Learning Lab. He is
advised by Prof. Fei-Fei Li,
co-advised by Prof. Scott Delp and Prof. Ehsan Adeli.
Previously, he worked with Prof. Weidong Cai at The University of Sydney,
where he was awarded the University Medal.
His research focuses on generative models and AI for healthcare.
He is a recipient of Stanford HAI fellowship and a finalist of the Qualcomm Innovation Fellowship.
Opportunities
I am actively looking for one student collaborator to co-lead the 3D Gen Playground project. Please drop me an email if you are a Stanford student and have experience in 3D vision and/or generative models.
Open Source Projects
A user-friendly codebase for accelerating 3D generation research. Features open data platform with standardized protocols, efficient data loaders, interactive 3DGS viewer, and plug-and-play components built on the GaussianVerse dataset.
Selected Publications
[* indicates equal contribution, † indicates equal mentorship.]
Repurposing 2D Diffusion Models with Gaussian Atlas for 3D Generation
2D diffusion models are also 3D content generators!
NeuHMR: Neural Rendering-Guided Human Motion Reconstruction
[3DV 2025]
[Paper]
[Code]
Human mesh recovery guided via generalizable neural rendering.
Wild2Avatar: Rendering Humans Behind Occlusions
Higher-fidelity human rendering from monocular occluded videos!
OccFusion: Rendering Occluded Humans with Generative Diffusion Priors
2D generative priors help inpaint occluded 3D humans.
Rendering Humans from Object-Occluded Monocular Videos
We rendered human from object occluded videos.
Exploiting Structural Consistency of Chest
Anatomy for Unsupervised Anomaly Detection
in Radiography Images
A faster and stronger network for chest X-ray anomaly detection.
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
We re-formulated unsupervised anomaly detection as semantic-sapce in-painting.
Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding
We decoded photo-realistic visual stimuli from fMRI brain signals.
DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
We achieved self-supervised MRI denoising through generative diffusion models.
Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
We proposed a geometry-aware feature aggregation operator for point cloud analysis.
Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond
An efficient and light-weight encoder-decoder network with SOTA performances.
BiX-NAS: Searching Efficient Bi-directional Architectures for Medical Image Segmentation
We proposed a NAS method to search efficient bi-directional architectures.
BiO-Net: Learning Recurrent Bidirectional Connections for Encoder-Decoder Architecture
We proposed bi-directional skip connections in the encoder-decoder architecture.
DSNet: A Weakly-Supervised Dual-Stream Framework for Effective Gigapixel Pathology Image Analysis
We proposed to combine global-local clues for weakly-supervised WSI analysis.
Preprints
[* indicates equal contribution]
Partial Graph Reasoning for Neural Network Regularization
[ArXiv]
[Paper]
Two-Stage Monte Carlo Denoising with Adaptive Sampling and Kernel Pool
[ArXiv]
[Paper]
Education
Ph.D. Student in Computer Science
Palo Alto, CA, U.S.
M.S. in Computer Science (petition during Ph.D.)
Palo Alto, CA, U.S.
B.S. in Computer Science and Technology (Advanced) (Honours)
Sydney, NSW, Australia
Service
Organizer:
GenAI4Health Workshop @ NeurIPS 2025
Conference reviewer:
CVPR (2021,2022,2023,2024,2025), MICCAI (2021,2022,2023,2024,2025), ICCV (2023,2025), ECCV (2022,2024), ICLR (2025,2026), ICML 2022, NeurIPS (2022,2024,2025), WACV (2023,2024,2025), AAAI 2025, AISTATS 2025.
Jounral reviewer:
IEEE TPAMI, IEEE TIP, IEEE TMI, IEEE IV, Neurocomputing, Scientific Reports.
Teaching assistant:
CS 231n (Stanford, Spring 2025 & 2024 & 2023), COMP 3419 (USYD, Fall 2019)
