Tianfu Wang 王天夫

I am a second year PhD student in the University of Maryland, College Park supervised by Prof. Christopher Metzler.

Previously I obtained my master's from ETH Zürich, supervised by Anton Obukhov and Prof. Konrad Schindler, and my bachelor's from Northwestern University.

I am interested in generative models for vision. More specifically, I work on leveraging diffusion models and other generative approaches for tasks such as depth estimation, image restoration, video synthesis, and 3D content creation. I am also interested in the intersection of generative AI with graphics, computational photography, and novel imaging systems.

I am actively looking for internship opportunities in the summer of 2026.

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Photo credit: Mingyang Xie

A camera-controlled video diffusion model that performs novel video trajectory synthesis by conditioning on 4D scene latents from a large 4D reconstruction model.

Single-step latent diffusion for jointly predicting clear restore underwater image and water medium backscattering and transmission.

A novel diffusion-based approach for dense prediction, providing foundation models and pipelines for a range of computer vision and image analysis tasks, including monocular depth estimation, surface normal prediction, and intrinsic image decomposition.

A robust 2D reflection removal method leveraging physical flash/no-flash cues with latent-space diffusion separation.

Fine-tuning a pre-trained video diffusion model on event camera data turns it into a state-of-the-art event-based video interpolater and extrapolator with high temporal resolution.

We present an accurate and fast gaze tracking method using single-shot phase-measuring-deflectometry (PMD), taking advantage of dense 3D surface measurements of the eye surface.

Using a differentiable renderer to simulate reflection light transport allows us to take advantage of dense sturctured screen illumination to accurately optimize eye gaze direction.

We specialize a pre-trained latent diffusion model into an efficient data generation pipeline with style control and multi-resolution semantic adherence, improving downstream domain generalization performance.

Our method lifts the power of generative 2D image models, such as Stable Diffusion, into 3D. Using them as a way to synthesize unseen content for novel views, and NeRF to reconcile all generated views, our method provides vivid painting of the input 3D assets in a variety of shape categories.

We present a first study on the trade-offs between projection and reflection based 3D imaging systems under diffusive and specular reflection mixtures. Experiments are conducted in our projection/reflection simulator framework using the Mitsuba2 renderer.

Cool Stuff

Metaballs meet Smooth Particle Hydrodynamics (SPH)

Misc

Aside from research, I delight in the symphony of flavors that dance on the tongue (good food) and the harmonious melodies that serenade the ears (music). A few of my musical heroes include 竇唯 (Dou Wei), Radiohead, Brian Eno, and Fleet Foxes.

This is a Jon Barron website. Thanks Jon!