2 citations · 3 across the 9 of their papers we have counts for
12 papers
MoNe: Modular Neural Memory for Efficient Long Context Inference
Wonguk Cho, Kyubyung Chae, Tribhuvanesh Orekondy +6
We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining. MoNe reads context in f…
Memory-Efficient Fine-Tuning Diffusion Transformers via Dynamic Patch Sampling and Block Skipping
Sunghyun Park, Jeongho Kim, Hyoungwoo Park +6
Diffusion Transformers (DiTs) have significantly enhanced text-to-image (T2I) generation quality, enabling high-quality personalized content creation. However, fine-tuning these mo…
SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection
Kyudan Jung, Jihwan Kim, Minwoo Lee +4
Recent advancements in text-to-speech technologies enable generating high-fidelity synthetic speech nearly indistinguishable from real human voices. While recent studies show the e…
Infinite-Homography as Robust Conditioning for Camera-Controlled Video Generation
Min-Jung Kim, Jeongho Kim, Hoiyeong Jin +2
Recent progress in video diffusion models has spurred growing interest in camera-controlled novel-view video generation for dynamic scenes, aiming to provide creators with cinemati…
InsertAnywhere: Geometrically Grounded and Optics-Aware Video Object Insertion
Hoiyeong Jin, Hyojin Jang, Junha Hyung +6
Recent advances in diffusion models have enabled impressive video editing capabilities, yet production-grade Video Object Insertion (VOI) remains challenging due to inadequate 4D s…
Memory-Efficient Personalization of Text-to-Image Diffusion Models via Selective Optimization Strategies
Seokeon Choi, Sunghyun Park, Hyoungwoo Park +2
Memory-efficient personalization is critical for adapting text-to-image diffusion models while preserving user privacy and operating within the limited computational resources of e…
From Wardrobe to Canvas: Wardrobe Polyptych LoRA for Part-level Controllable Human Image Generation
Jeongho Kim, Sunghyun Park, Hyoungwoo Park +3
Recent diffusion models achieve personalization by learning specific subjects, allowing learned attributes to be integrated into generated images. However, personalized human image…
MultiHuman-Testbench: Benchmarking Image Generation for Multiple Humans
Shubhankar Borse, Seokeon Choi, Sunghyun Park +6
Generation of images containing multiple humans, performing complex actions, while preserving their facial identities, is a significant challenge. A major factor contributing to th…
PromptDresser: Improving the Quality and Controllability of Virtual Try-On via Generative Textual Prompt and Prompt-aware Mask
Jeongho Kim, Hoiyeong Jin, Sunghyun Park +1
Recent virtual try-on approaches have advanced by finetuning pre-trained text-to-image diffusion models to leverage their powerful generative ability. However, the use of text prom…
What to Preserve and What to Transfer: Faithful, Identity-Preserving Diffusion-based Hairstyle Transfer
Chaeyeon Chung, Sunghyun Park, Jeongho Kim +1
Hairstyle transfer is a challenging task in the image editing field that modifies the hairstyle of a given face image while preserving its other appearance and background features.…
TCAN: Animating Human Images with Temporally Consistent Pose Guidance using Diffusion Models
Jeongho Kim, Min-Jung Kim, Junsoo Lee +1
Pose-driven human-image animation diffusion models have shown remarkable capabilities in realistic human video synthesis. Despite the promising results achieved by previous approac…
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…