Gordon Guocheng Qian — accepted AI-conference papers

2026

EasyV2V: A High-quality Instruction-based Video Editing Framework

Jinjie Mai, Chaoyang Wang, Gordon Guocheng Qian, Willi Menapace, Sergey Tulyakov, Bernard Ghanem +2

CVPR 2026

While image editing has advanced rapidly, video editing remains less explored, facing challenges in consistency, control, and generalization.We study the design space of data, architecture, and control, and introduce EasyV2V, a simple and effective framework for instruction-based video editing. On t

Cited by 4 Source ↗code ↗Scholar ↗

2026

Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization

Tsai-Shien Chen, Aliaksandr Siarohin, Gordon Guocheng Qian, Kuan-Chieh Jackson Wang, Egor Nemchinov, Moayed Haji-Ali +6

CVPR 2026

Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen contexts. However, existing methods rely on holistic embeddings from general-purpose image encoders, which entangle multiple visual factors and make it diffi

Cited by 0 Source ↗code ↗Scholar ↗