#CVPR 2023

The project introduces advancements in multi-modal face generation and editing through pre-trained uni-modal diffusion models. It allows precise generation and editing of high-quality facial images via text and segmentation mask controls, focusing on identity preservation and dynamic diffusion. Notable updates involve FreeU integration and comprehensive training pipelines. This repository is a valuable resource for researchers and developers in facial image synthesis and modification.

This article presents a method for generating realistic talking head videos through the use of single portrait images combined with audio inputs. The technique involves using advanced 3D motion coefficient learning to produce lifelike facial animations. The project is integrated with platforms like Discord, allowing for the creation of high-quality videos from text inputs at no cost. It is compatible with multiple operating systems such as Windows, Linux, macOS, and Docker, broadening its accessibility. Recent updates have improved image quality with new full-body modes and enhancements. Being open-source, the project allows community involvement and feature expansions. With enriched WebUI demonstrations and upgraded performance, it serves as a crucial tool for developers involved in facial animation and multimedia projects.

Explore state-of-the-art techniques in generalized referring expression segmentation for precise object identification in complex visual environments. Utilizing models like ResNet-50 and Swin-Tiny with technologies such as Detectron2, this project offers enhanced segmentation accuracy. Gain insights into comprehensive configurations and leverage large-scale datasets to advance video segmentation outcomes. Stay informed on recent updates in this dynamic area for optimal performance in referring expression tasks.

Employ C2PNet's physics-aware dehazing methods with contrastive regularization to refine image clarity and detail. Utilizing PyTorch, this project offers detailed guidance on setting up and preparing datasets through pre-trained models, applicable for indoor and outdoor scenarios.

Learn how Custom Diffusion enables efficient fine-tuning of text-to-image models like Stable Diffusion. This approach introduces new concepts into models by adjusting key parameters, resulting in unique, multi-concept images with minimal storage impact. Access newly released datasets and utilize swift processing capabilities, now available in diffusers for improved training and inference.