1 citations · 3 across the 12 of their papers we have counts for
7 papers · 1 filter
SUGAR: Subject-Driven Video Customization in a Zero-Shot Manner
Yufan Zhou, Ruiyi Zhang, Jiuxiang Gu +3
We present SUGAR, a zero-shot method for subject-driven video customization. Given an input image, SUGAR is capable of generating videos for the subject contained in the image and…
UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics
Xi Chen, Zhifei Zhang, He Zhang +10
We introduce UniReal, a unified framework designed to address various image generation and editing tasks. Existing solutions often vary by tasks, yet share fundamental principles:…
LayerFusion: Harmonized Multi-Layer Text-to-Image Generation with Generative Priors
Yusuf Dalva, Yijun Li, Qing Liu +4
Large-scale diffusion models have achieved remarkable success in generating high-quality images from textual descriptions, gaining popularity across various applications. However,…
GroupDiff: Diffusion-based Group Portrait Editing
Yuming Jiang, Nanxuan Zhao, Qing Liu +4
Group portrait editing is highly desirable since users constantly want to add a person, delete a person, or manipulate existing persons. It is also challenging due to the intricate…
Toffee: Efficient Million-Scale Dataset Construction for Subject-Driven Text-to-Image Generation
Yufan Zhou, Ruiyi Zhang, Kaizhi Zheng +5
In subject-driven text-to-image generation, recent works have achieved superior performance by training the model on synthetic datasets containing numerous image pairs. Trained on…
SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual Editing
Jing Gu, Nanxuan Zhao, Wei Xiong +7
Effective editing of personal content holds a pivotal role in enabling individuals to express their creativity, weaving captivating narratives within their visual stories, and elev…
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising
Maham Tanveer, Yizhi Wang, Ruiqi Wang +3
We present AnaMoDiff, a novel diffusion-based method for 2D motion analogies that is applied to raw, unannotated videos of articulated characters. Our goal is to accurately transfe…