2 citations · 5 across the 16 of their papers we have counts for
18 papers · 1 filter
Can Learning to Answer Spatial Questions Improve Novel-View Generation?
Jahyeok Koo, Hyeonseo Yu, Seungho Jang +4
Generating novel views from limited observations requires preserving both the appearance and spatial structure of a scene. Although recent approaches exploit fine-grained geometric…
Transferability Between Understanding and Generation in Unified Multimodal Models
Jiwon Kang, Heeji Yoon, Jaewoo Jung +5
Unified Multimodal Models (UMMs) integrate image understanding and generation within a single architecture, yet how the two tasks interact remains understudied. We investigate $\bo…
MVTrack4Gen: Multi-View Point Tracking as Geometric Supervision for 4D Video Generation
JoungBin Lee, Jaewoo Jung, Jongmin Lee +8
Synthesizing a novel-view video from a monocular reference video along a target camera trajectory requires both geometric consistency and motion fidelity with respect to the refere…
Entropy-Gradient Grounding: Training-Free Evidence Retrieval in Vision-Language Models
Marcel Gröpl, Jaewoo Jung, Seungryong Kim +2
Despite rapid progress, pretrained vision-language models still struggle when answers depend on tiny visual details or on combining clues spread across multiple regions, as in docu…
Emergent Outlier View Rejection in Visual Geometry Grounded Transformers
Jisang Han, Sunghwan Hong, Jaewoo Jung +5
Reliable 3D reconstruction from in-the-wild image collections is often hindered by "noisy" images-irrelevant inputs with little or no view overlap with others. While traditional St…
C3G: Learning Compact 3D Representations with 2K Gaussians
Honggyu An, Jaewoo Jung, Mungyeom Kim +10
Reconstructing and understanding 3D scenes from unposed sparse views in a feed-forward manner remains as a challenging task in 3D computer vision. Recent approaches use per-pixel 3…
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…
3D Scene Prompting for Scene-Consistent Camera-Controllable Video Generation
JoungBin Lee, Jaewoo Jung, Jisang Han +6
We present 3DScenePrompt, a framework that generates the next video chunk from arbitrary-length input while enabling precise camera control and preserving scene consistency. Unlike…
DUSt3R: Enhancing 3D Reconstruction for Dynamic Scenes
Jisang Han, Honggyu An, Jaewoo Jung +7
In this work, we address the task of 3D reconstruction in dynamic scenes, where object motions frequently degrade the quality of previous 3D pointmap regression methods, such as DU…
Cross-View Completion Models are Zero-shot Correspondence Estimators
Honggyu An, Jinhyeon Kim, Seonghoon Park +4
In this work, we explore new perspectives on cross-view completion learning by drawing an analogy to self-supervised correspondence learning. Through our analysis, we demonstrate t…
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 Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence
Sunghwan Hong, Seokju Cho, Seungryong Kim +1
This paper introduces a Transformer-based integrative feature and cost aggregation network designed for dense matching tasks. In the context of dense matching, many works benefit f…
Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting
Jaewoo Jung, Jisang Han, Honggyu An +3
3D Gaussian splatting (3DGS) has recently demonstrated impressive capabilities in real-time novel view synthesis and 3D reconstruction. However, 3DGS heavily depends on the accurat…
Context Enhanced Transformer for Single Image Object Detection
Seungjun An, Seonghoon Park, Gyeongnyeon Kim +3
With the increasing importance of video data in real-world applications, there is a rising need for efficient object detection methods that utilize temporal information. While exis…
Self-Evolving Neural Radiance Fields
Jaewoo Jung, Jisang Han, Jiwon Kang +3
Recently, neural radiance field (NeRF) has shown remarkable performance in novel view synthesis and 3D reconstruction. However, it still requires abundant high-quality images, limi…
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…
Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters
Gyuseong Lee, Wooseok Jang, Jinhyeon Kim +2
Learning robust vision models that perform well in out-of-distribution (OOD) situations is an important task for model deployment in real-world settings. Despite extensive research…