DITTO: Dual and Integrated Latent Topologies for Implicit 3D Reconstruction (CVPR 2024)

Jaehyeok Shim, Kyungdon Joo

DITTO addresses a crucial task in 3D computer vision: implicit 3D reconstruction from point clouds. DITTO has significantly enhanced 3D understanding ability by leveraging latent features in two modalities: grid and point latents. The core innovation lies in exploiting the complementary synergy between these two types of latents. Our contribution is effectively integration of these two latent types within a network architecture, improving 3D reconstruction performance. Our research holds significant potential for various applications involving implicit fields.

ContactGen: Contact-Guided Interactive 3D Human Generation for Partners (AAAI 2024)

Dongjun Gu, Jaehyeok Shim, Jaehoon Jang, Changwoo Kang, Kyungdon Joo

Contactgen introduces a novel approach for generating 3D human poses that interact realistically with a given another human. We utilize a guided diffusion framework, optimizing human poses to ensure physically plausible interactions. This optimization is based on the predicted contact area determined by the given type of interaction.

SDF-Diffusion: Diffusion-Based Signed Distance Fields for 3D Shape Generation (CVPR 2023)

Jaehyeok Shim, Changwoo Kang, Kyungdon Joo

SDF-Diffusion is a 3D shape generation framework using diffusion models and signed distance fields for continuous 3D representations (such as meshes), offering high-resolution shapes in a two-stage process (generation and super-resolution) and demonstrating competitive performance on unconditional and conditional 3D shape generation tasks.