7 citations · 14 across the 11 of their papers we have counts for
12 papers
Imagine3D-LLM: Teaching MLLMs to Imagine 3D Scenes Before Answering
Jaewoo Jung, Hyeonseo Yu, Honggyu An +10
Reasoning about the 3D world from multi-view images remains a fundamental challenge for Multimodal Large Language Models (MLLMs). While modern MLLMs handle single-image inputs effe…
Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization
Paul Hyunbin Cho, Jinhyuk Jang, SeokYoung Lee +7
Diffusion-based lip synchronization models achieve strong visual quality and audio-visual alignment, but full-sequence bidirectional attention and many denoising steps make them im…
AgentRVOS: Reasoning over Object Tracks for Zero-Shot Referring Video Object Segmentation
Woojeong Jin, Jaeho Lee, Heeseong Shin +3
Referring Video Object Segmentation (RVOS) aims to segment a target object throughout a video given a natural language query. Training-free methods for this task follow a common pi…
Visual Representation Alignment for Multimodal Large Language Models
Heeji Yoon, Jaewoo Jung, Junwan Kim +10
Multimodal large language models (MLLMs) trained with visual instruction tuning have achieved strong performance across diverse tasks, yet they remain limited in vision-centric tas…
Exploring Conditions for Diffusion models in Robotic Control
Heeseong Shin, Byeongho Heo, Dongyoon Han +2
While pre-trained visual representations have significantly advanced imitation learning, they are often task-agnostic as they remain frozen during policy learning. In this work, we…
Seg4Diff: Unveiling Open-Vocabulary Segmentation in Text-to-Image Diffusion Transformers
Chaehyun Kim, Heeseong Shin, Eunbeen Hong +5
Text-to-image diffusion models excel at translating language prompts into photorealistic images by implicitly grounding textual concepts through their cross-modal attention mechani…
S^4M: Boosting Semi-Supervised Instance Segmentation with SAM
Heeji Yoon, Heeseong Shin, Eunbeen Hong +4
Semi-supervised instance segmentation poses challenges due to limited labeled data, causing difficulties in accurately localizing distinct object instances. Current teacher-student…
PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting
Sunghwan Hong, Jaewoo Jung, Heeseong Shin +4
We consider the problem of novel view synthesis from unposed images in a single feed-forward. Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstructi…
Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels
Heeseong Shin, Chaehyun Kim, Sunghwan Hong +4
Large-scale vision-language models like CLIP have demonstrated impressive open-vocabulary capabilities for image-level tasks, excelling in recognizing what objects are present. How…
Unifying Correspondence, Pose and NeRF for Pose-Free Novel View Synthesis from Stereo Pairs
Sunghwan Hong, Jaewoo Jung, Heeseong Shin +3
This work delves into the task of pose-free novel view synthesis from stereo pairs, a challenging and pioneering task in 3D vision. Our innovative framework, unlike any before, sea…
DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation
Susung Hong, Junyoung Seo, Heeseong Shin +2
In the paradigm of AI-generated content (AIGC), there has been increasing attention to transferring knowledge from pre-trained text-to-image (T2I) models to text-to-video (T2V) gen…
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation
Seokju Cho, Heeseong Shin, Sunghwan Hong +3
Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions. In this work, we introduce a novel c…