6 papers · 1 filter
EgoX: Egocentric Video Generation from a Single Exocentric Video
Taewoong Kang, Kinam Kim, Dohyeon Kim +3
Egocentric perception enables humans to experience and understand the world directly from their own point of view. Translating exocentric (third-person) videos into egocentric (fir…
Cross-Frame Representation Alignment for Fine-Tuning Video Diffusion Models
Sungwon Hwang, Hyojin Jang, Kinam Kim +2
Fine-tuning Video Diffusion Models (VDMs) at the user level to generate videos that reflect specific attributes of training data presents notable challenges, yet remains underexplo…
SphereDiff: Tuning-free 360° Static and Dynamic Panorama Generation via Spherical Latent Representation
Minho Park, Taewoong Kang, Jooyeol Yun +2
The increasing demand for AR/VR applications has highlighted the need for high-quality content, such as 360° live wallpapers. However, generating high-quality 360° panoramic conten…
CA-LoRA: Concept-Aware LoRA for Domain-Aligned Segmentation Dataset Generation
Minho Park, Sunghyun Park, Jungsoo Lee +5
This paper addresses the challenge of data scarcity in semantic segmentation by generating datasets through text-to-image (T2I) generation models, reducing image acquisition and la…
Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language Models
Minho Park, Sunghyun Park, Jooyeol Yun +1
Recent advancements in text-to-image generation have inspired researchers to generate datasets tailored for perception models using generative models, which prove particularly valu…
StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On
Jeongho Kim, Gyojung Gu, Minho Park +2
Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the…