Yuanbo Xiangli

I am a postdoctoral researcher at Cornell University, advised by Prof. Noah Snavely. Previously, I obtained my Ph.D at Multimedia Lab, Information Engineering, CUHK, supervised by Prof. Dahua Lin.

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My research interest lies in 3D computer vision and deep generative modeling. Currently, I am working on photorealistic and efficient city scenes reconstruction, manipulation and generation based on multi-source data, including satellite imagery, oblique photography, street view panoramas and urban planning information.

A dual-branch system enhances rendering and reconstruction at the same time, with the mutual geometry regularization and guidance between Gaussain primitives and neural surface.

Neural Gaffer is an end-to-end 2D relighting diffusion model that accurately relights any object in a single image under various lighting conditions.

High-quality 3D Gaussian primitives from 2-4 posed sparse images within 0.23 seconds.

Scaffold-GS uses anchor points to distribute local 3D Gaussians, and predicts their attributes on-the-fly based on viewing direction and distance within the view frustum.

A novel neural scene representation that learns a set of object-aware ground feature planes, where an asset library storing template feature patches can be constructed in an unsupervised manner. The representation enables flexible and intuitive 3D scene editing at instance-, category- and scene-level.

A large-scale, comprehensive, and high-quality synthetic dataset for city-scale neural rendering researches. Leveraging the Unreal Engine 5 City Sample project, we developed a pipeline to easily collect aerial and street city views with ground-truth camera poses, as well as a series of additional data modalities. Flexible control on environmental factors like light, weather, human and car crowd is also available in our pipeline, supporting the need of various tasks covering city-scale neural rendering and beyond.

This work targets at modeling vast-spanned urban regions and operates on real-world data sources. We use grid features to profile the scene and a light-weighted NeRF to pick up details. The two-branch model can produce photo-realistic results with high rendering speed.

A new dataset containing multi-view satellite images and street-level panoramas, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geo-locations.

An attempt to bring NeRF to potentially city-scale scenes, which requires rendering drastically varied observations (level-of-detail and spatial coverage) at multiscales.

Use graph-based VAE to automatically learn from large amount of vectorized public urban planning data for fast generation of batches of diverse and valid city block templates.

The proposed realness distribution provides stronger guidance to the generator and encourages it to learn more diverse outputs; enables the simplest GAN structure to synthesis high resolution portrait for the first time, with affordable computational overhead.

We explore the feasibility of the compound identity leakage across cyber-physical spaces and unveil that co-located smart device IDs (e.g., smartphone MAC addresses) and physical biometrics (e.g., facial/vocal samples) are side channels to each other. An attacker can comprehensively profile victims in multi-dimension with nearly zero analysis effort.

The proposed framework leverages the abundant side-channel information provided by the ubiquitous IoT environment in mordern life, enabling the construction of an in-domain speaker recognition model with zero human enrollment.

The website template was borrowed from Jon Baron. Thanks for the generosity :)