2 citations · 3 across the 9 of their papers we have counts for
10 papers
VideoRFSplat: Direct Scene-Level Text-to-3D Gaussian Splatting Generation with Flexible Pose and Multi-View Joint Modeling
Hyojun Go, Byeongjun Park, Hyelin Nam +3
We propose VideoRFSplat, a direct text-to-3D model leveraging a video generation model to generate realistic 3D Gaussian Splatting (3DGS) for unbounded real-world scenes. To genera…
SteerX: Creating Any Camera-Free 3D and 4D Scenes with Geometric Steering
Byeongjun Park, Hyojun Go, Hyelin Nam +3
Recent progress in 3D/4D scene generation emphasizes the importance of physical alignment throughout video generation and scene reconstruction. However, existing methods improve th…
SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis
Hyojun Go, Byeongjun Park, Jiho Jang +3
Text-based generation and editing of 3D scenes hold significant potential for streamlining content creation through intuitive user interactions. While recent advances leverage 3D G…
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation
Seokil Ham, Hee-Seon Kim, Sangmin Woo +1
Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain large…
Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts
Byeongjun Park, Hyojun Go, Jin-Young Kim +3
Diffusion models have achieved remarkable success across a range of generative tasks. Recent efforts to enhance diffusion model architectures have reimagined them as a form of mult…
HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D
Sangmin Woo, Byeongjun Park, Hyojun Go +2
Recent progress in single-image 3D generation highlights the importance of multi-view coherency, leveraging 3D priors from large-scale diffusion models pretrained on Internet-scale…
Modality Mixer Exploiting Complementary Information for Multi-modal Action Recognition
Sumin Lee, Sangmin Woo, Muhammad Adi Nugroho +1
Due to the distinctive characteristics of sensors, each modality exhibits unique physical properties. For this reason, in the context of multi-modal action recognition, it is impor…
DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection
Se-Ho Kim, Inyong Koo, Inyoung Lee +2
Denoising diffusion models show remarkable performances in generative tasks, and their potential applications in perception tasks are gaining interest. In this paper, we introduce…
Point-DynRF: Point-based Dynamic Radiance Fields from a Monocular Video
Byeongjun Park, Changick Kim
Dynamic radiance fields have emerged as a promising approach for generating novel views from a monocular video. However, previous methods enforce the geometric consistency to dynam…
Denoising Task Routing for Diffusion Models
Byeongjun Park, Sangmin Woo, Hyojun Go +2
Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent…