2 citations · 5 across the 67 of their papers we have counts for
24 papers · 1 filter
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…
Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure
Jooyeol Yun, Jaegul Choo
Scalable Vector Graphics (SVG) are central to modern web design, and the demand to animate them continues to grow as web environments become increasingly dynamic. Yet automating th…
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…
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…
ConceptScope: Characterizing Dataset Bias via Disentangled Visual Concepts
Jinho Choi, Hyesu Lim, Steffen Schneider +1
Dataset bias, where data points are skewed to certain concepts, is ubiquitous in machine learning datasets. Yet, systematically identifying these biases is challenging without cost…
PHUMA: Physically Reliable Humanoid Locomotion Dataset
Kyungmin Lee, Sibeen Kim, Youngdo Lee +6
Motion imitation is a promising approach for humanoid locomotion, enabling agents to acquire humanlike behaviors. Existing methods typically rely on high-quality motion capture dat…
ACG: Action Coherence Guidance for Flow-based Vision-Language-Action models
Minho Park, Kinam Kim, Junha Hyung +5
Diffusion and flow matching models have emerged as powerful robot policies, enabling Vision-Language-Action (VLA) models to generalize across diverse scenes and instructions. Yet,…
RL makes MLLMs see better than SFT
Junha Song, Sangdoo Yun, Dongyoon Han +2
A dominant assumption in Multimodal Language Model (MLLM) research is that its performance is largely inherited from the LLM backbone, given its immense parameter scale and remarka…
Reward-Weighted Sampling: Enhancing Non-Autoregressive Characteristics in Masked Diffusion LLMs
Daehoon Gwak, Minseo Jung, Junwoo Park +4
Masked diffusion models (MDMs) offer a promising non-autoregressive alternative for large language modeling. Standard decoding methods for MDMs, such as confidence-based sampling,…
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
Qiuyu Chen, Xin Jin, Yue Song +45
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…
Devil is in the Detail: Towards Injecting Fine Details of Image Prompt in Image Generation via Conflict-free Guidance and Stratified Attention
Kyungmin Jo, Jooyeol Yun, Jaegul Choo
While large-scale text-to-image diffusion models enable the generation of high-quality, diverse images from text prompts, these prompts struggle to capture intricate details, such…
MM-SeR: Multimodal Self-Refinement for Lightweight Image Captioning
Junha Song, Yongsik Jo, So Yeon Min +4
Systems such as video chatbots and navigation robots often depend on streaming image captioning to interpret visual inputs. Existing approaches typically employ large multimodal la…
DesignLab: Designing Slides Through Iterative Detection and Correction
Jooyeol Yun, Heng Wang, Yotaro Shimose +2
Designing high-quality presentation slides can be challenging for non-experts due to the complexity involved in navigating various design choices. Numerous automated tools can sugg…
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…
Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age assessment
Jinhee Kim, Taesung Kim, Taewoo Kim +5
In pediatric orthodontics, accurate estimation of growth potential is essential for developing effective treatment strategies. Our research aims to predict this potential by identi…
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…
Fair Generation without Unfair Distortions: Debiasing Text-to-Image Generation with Entanglement-Free Attention
Jeonghoon Park, Juyoung Lee, Chaeyeon Chung +3
Recent advancements in diffusion-based text-to-image (T2I) models have enabled the generation of high-quality and photorealistic images from text. However, they often exhibit socie…
Good Noise Makes Good Edits: A Training-Free Diffusion-Based Video Editing with Image and Text Prompts
Saemee Choi, Sohyun Jeong, Hyojin Jang +2
We propose VINO, the first zero-shot, training-free video editing method conditioned on both image and text. Our approach introduces -start sampling and dilated dual masking to…
Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models
Cheonbok Park, Jeonghoon Kim, Joosung Lee +3
Reinforcement learning with verifiable reward (RLVR) has been instrumental in eliciting strong reasoning capabilities from large language models (LLMs) via long chains of thought (…
Temporal In-Context Fine-Tuning with Temporal Reasoning for Versatile Control of Video Diffusion Models
Kinam Kim, Junha Hyung, Jaegul Choo
Recent advances in text-to-video diffusion models have enabled high-quality video synthesis, but controllable generation remains challenging, particularly under limited data and co…
Exploring In-context Example Generation for Machine Translation
Dohyun Lee, Seungil Chad Lee, Chanwoo Yang +2
Large language models (LLMs) have demonstrated strong performance across various tasks, leveraging their exceptional in-context learning ability with only a few examples. According…
Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models
Junwoo Park, Hyuck Lee, Dohyun Lee +2
Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time-series forecastin…
Talk to Your Slides: High-Efficiency Slide Editing via Language-Driven Structured Data Manipulation
Kyudan Jung, Hojun Cho, Jooyeol Yun +3
Editing presentation slides is a frequent yet tedious task, ranging from creative layout design to repetitive text maintenance. While recent GUI-based agents powered by Multimodal…
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…