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KAIST Visual AI Group

The KAIST Visual AI Group, led by Minhyuk Sung, researches advancing technologies for processing, analyzing, and generalizing diverse visual data. Our research spans areas such as computer graphics, computer vision, and machine learning.

Research Highlights

SyncTweedies: A General Generative Framework Based on Synchronized Diffusions (NeurIPS 2024)
A novel approach for synchronizing multiple reverse diffusion processes to generate diverse visual content.

Neural Pose Representation Learning for Generating and Transferring Non-Rigid Object Poses (NeurIPS 2024)
A framework for learning neural pose representations that facilitate the generation and transfer of non-rigid object poses.

Occupancy-Based Dual Contouring (SIGGRAPH Asia 2024)
A dual contouring method that provides state-of-the-art performance for various neural implicit functions.

ReGround: Improving Textual and Spatial Grounding at No Cost (ECCV 2024)
A cost-free network reconfiguration for improving the text-prompt fidelity in layout-guided image generation.

Posterior Distillation Sampling (CVPR 2024)
A novel optimization method for editing parameterized images, applicable to NeRF, 3D Gaussian Splatting, and SVG.

As-Plausible-As-Possible: Plausibility-Aware Mesh Deformation Using 2D Diffusion Priors (CVPR 2024)
A plausibility-aware mesh deformation framework integrating Jacobian-based geometry representation and generative image priors.

SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions (NeurIPS 2023)
A zero-shot plug-and-play module that synchronizes multiple reverse diffusion processes, producing coherent images of various sizes.

SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulation (ICCV 2023)
A cascaded diffusion model based on a part-level implicit 3D representation.

PartGlot: Learning Shape Part Segmentation from Language Reference Games (CVPR 2022 (Oral))
A neural framework for learning semantic part segmentation of 3D shape geometry based solely on part referential language.

OptCtrlPoints: Finding the Optimal Control Points for Biharmonic 3D Shape Deformation (Pacific Graphics 2023)
A data-driven framework identifying the optimal sparse set of control points for biharmonic 3D shape deformation.

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