CVPR 2023 – Diffusion-SDF: Text-to-Shape via Voxelized Diffusion

In this episode we discuss Diffusion-SDF: Text-to-Shape via Voxelized Diffusion
by Muheng Li, Yueqi Duan, Jie Zhou, Jiwen Lu. The paper presents a new generative 3D modeling framework called Diffusion-SDF for synthesizing 3D shapes from text. The proposed framework uses a SDF autoencoder and Voxelized Diffusion model to generate representations for voxelized signed distance fields (SDFs) of 3D shapes. The researchers developed a novel UinU-Net architecture that improves the reconstruction of patch-independent SDF representations, enabling better text-to-shape synthesis. The results show that the Diffusion-SDF approach generates higher quality and diversified 3D shapes that conform well to given text descriptions, outperforming previous approaches.