How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
[ECCV2024] Video Foundation Models & Data for Multimodal Understanding
The main features of opengvlab/internvideo are: Video Generation, Pre-training Datasets.
Projects with overlapping indexed features include: plexpt/chatgpt-corpus — This project provides a comprehensive Chinese language corpus designed to support the training and fine-tuning of… aim-uofa/gendef. ali-vilab/vace — VACE is a set of software tools and frameworks for reference-guided video generation, diffusion-based editing, and… alibaba/animate-anything. alpha-vllm/lumina-t2x — Lumina-T2X is a unified framework for Text to Any Modality Generation. ailab-cvc/videocrafter — Videocrafter is a latent diffusion model designed for AI video synthesis. It functions as both a text-to-video and…
This project provides a comprehensive Chinese language corpus designed to support the training and fine-tuning of large language models. It serves as a structured natural language processing resource, offering a collection of text data that includes dialogue, customer service interactions, and creative writing. The dataset is organized into distinct thematic categories, allowing for targeted model development across specific conversational and narrative contexts. By providing information in standardized, schema-agnostic text formats, the collection ensures portability across various machine l
VACE is a set of software tools and frameworks for reference-guided video generation, diffusion-based editing, and video-to-video translation. It provides utilities to produce new video content and modify existing sequences by using reference materials to guide visual style, subject matter, and composition. The framework enables video-to-video translation and synthesis, allowing for the update of visual styles and depth. It also functions as a video editor for modifying properties and content through reference-guided transformations. The system covers localized video editing and inpainting,
Videocrafter is a latent diffusion model designed for AI video synthesis. It functions as both a text-to-video and image-to-video generation system, synthesizing high-quality video sequences from descriptive text prompts or static image inputs. The model utilizes a diffusion-based neural network to transform inputs into animated content, ensuring visual consistency and temporal coherence throughout the generated sequences. This allows for the creation of custom video clips and the animation of static images into fluid motion.