Hello 👋, I'm Gabriel (Jiahui) Huang

I’m a Research Scientist at Adobe Research, working on topics related to video processing and editing.

I live in the beautiful city of Vancouver, Canada 🇨🇦, where I previously received my Master’s degree from UBC under Leonid Sigal.

Highlights

Publications

Unified Video Dense Prediction from Disjoint Data

ECCV, 2026

UniD takes an input video and jointly produces eight dense scene predictions in a single forward pass — while trained from disjoint, domain-specific datasets with no annotation overlap.

DAGE: Dual-Stream Architecture for Efficient and Fine-Grained Geometry Estimation

CVPR, 2026

A dual-stream transformer that decouples global view consistency from fine-detail preservation for high-resolution multi-view geometry estimation.

Generative Video Motion Editing with 3D Point Tracks

CVPR, 2026

A framework for precise video motion editing via 3D point tracks, enabling joint control over camera and object motion.

VideoMaMa: Mask-Guided Video Matting via Generative Prior

CVPR, 2026

Turns coarse segmentation masks into pixel-accurate alpha mattes by leveraging pretrained video diffusion priors, with zero-shot generalization to in-the-wild footage.

AnthroTAP: Learning Point Tracking with Real-World Motion

CVPR, 2026

Generates pseudo-labeled point-tracking training data by exploiting human-motion complexity captured via the SMPL model.

FlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking

FlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking

CVPR, 2024

Dense point tracking on long-range videos.

Single Image Video Prediction with Auto-Regressive GANs

Sensors, Volume 22

Animating facial expressions using GANs in an auto-regressive fashion.

Deep Anchored Convolutional Neural Networks

Jiahui Huang, Kshitij Dwivedi, Gemma Roig
CVPR Workshops, 2019 Oral

A highly efficitn neural network architecture that reuses weights from different layres.