- Wan: Open and Advanced Large-Scale Video Generative Models
👍 Multiple Tasks: Wan2 1 excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation 👍 Visual Text Generation: Wan2 1 is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications
- GitHub - k4yt3x video2x: A machine learning-based video super . . .
A machine learning-based video super resolution and frame interpolation framework Est Hack the Valley II, 2018 - k4yt3x video2x
- Video-R1: Reinforcing Video Reasoning in MLLMs - GitHub
Video-R1 significantly outperforms previous models across most benchmarks Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-R1-7B achieves a new state-of-the-art accuracy of 35 8%, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters
- HunyuanCustom: A Multimodal-Driven Architecture for Customized Video . . .
Customized video generation aims to produce videos featuring specific subjects under flexible user-defined conditions, yet existing methods often struggle with identity consistency and limited input modalities In this paper, we propose HunyuanCustom, a multi-modal customized video generation
- DepthAnything Video-Depth-Anything - GitHub
This work presents Video Depth Anything based on Depth Anything V2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher
- Lightricks LTX-Video: Official repository for LTX-Video - GitHub
LTX-Video is the first DiT-based video generation model that can generate high-quality videos in real-time It can generate 30 FPS videos at 1216×704 resolution, faster than it takes to watch them It can generate 30 FPS videos at 1216×704 resolution, faster than it takes to watch them
- 【EMNLP 2024 】Video-LLaVA: Learning United Visual . . . - GitHub
[2024 09 25] 🔥🔥🔥 Our Video-LLaVA has been accepted at EMNLP 2024! We earn the meta score of 4 [2024 07 27] 🔥🔥🔥 A fine-tuned Video-LLaVA focuses on theme exploration, narrative analysis, and character dynamics
- stepfun-ai Step-Video-T2V - GitHub
We present Step-Video-T2V, a state-of-the-art (SoTA) text-to-video pre-trained model with 30 billion parameters and the capability to generate videos up to 204 frames To enhance both training and inference efficiency, we propose a deep compression VAE for videos, achieving 16x16 spatial and 8x temporal compression ratios
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