2 citations · 3 across the 18 of their papers we have counts for
20 papers
GenRec: Knowing Where to Reconstruct and Where to Generate
Ata Çelen, Jaewoo Jung, Federico Tombari +4
Generative novel view synthesis from sparse input images is rarely all reconstruction or all generation: pixels visible in some source view have a unique correct value modulated on…
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…
Geometric Action Model for Robot Policy Learning
Jisang Han, Seonghu Jeon, Jaewoo Jung +7
Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world. Recent vision-language-acti…
Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction
Mungyeom Kim, Minkyeong Jeon, Honggyu An +10
Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians pixel-wise for each frame,…
TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking
Jisu Nam, Jahyeok Koo, Soowon Son +4
Dense 3D tracking from monocular video is fundamental to dynamic scene understanding. While recent 3D foundation models provide reliable per-frame geometry, recovering object motio…
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…
Vid-CamEdit: Video Camera Trajectory Editing with Generative Rendering from Estimated Geometry
Junyoung Seo, Jisang Han, Jaewoo Jung +9
We introduce Vid-CamEdit, a novel framework for video camera trajectory editing, enabling the re-synthesis of monocular videos along user-defined camera paths. This task is challen…
URECA: Unique Region Caption Anything
Sangbeom Lim, Junwan Kim, Heeji Yoon +2
Region-level captioning aims to generate natural language descriptions for specific image regions while highlighting their distinguishing features. However, existing methods strugg…
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…
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…
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…