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KAIST Visual AI Group

The KAIST Visual AI Group, led by Minhyuk Sung, focuses on researching and advancing AI technologies specialized in processing, analyzing, and generalizing a variety of visual data, including 2D images and videos, 3D shapes, and 4D animations of 3D objects.

Research Highlights

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

ReGround: Improving Textual and Spatial Grounding at No Cost (arXiv 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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