P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising

In this work, we tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schr\"odinger bridges to points clouds. Unlike previous approaches that predict point-wise displacements from point features or learned noise distributions, our method learns an optimal transport plan between paired point clouds. Experiments on object datasets like PU-Net and real-world datasets such as ScanNet++ and ARKitScenes show that P2P-Bridge achieves significant improvements over existing methods. While our approach demonstrates strong results using only point coordinates, we also show that incorporating additional features, such as color information or point-wise DINOv2 features, further enhances the performance. Code and pretrained models are available at https://p2p-bridge.github.io.

A Study on Altering the Latent Space of Pretrained Text to Speech Models for Improved Expressiveness

This report explores the challenge of enhancing expressiveness control in Text-to-Speech (TTS) models by augmenting a frozen pretrained model with a Diffusion Model that is conditioned on joint semantic audio/text embeddings. The paper identifies the challenges encountered when working with a VAE-based TTS model and evaluates different image-to-image methods for altering latent speech features. Our results offer valuable insights into the complexities of adding expressiveness control to TTS systems and open avenues for future research in this direction.