The Best 190 Video Processing Tools in 2025

TimeSformer is a video classification model pre-trained on the Kinetics-400 dataset, utilizing a spatiotemporal attention mechanism for video understanding.

ViViT is an extension of the Vision Transformer (ViT) for video processing, particularly suitable for video classification tasks.

Dynamic LoRAs can add specific types of motion effects to animations, such as zooming, panning, tilting, and rotation.

VideoMAE is a video self-supervised pretraining model based on Masked Autoencoder (MAE), which learns internal video representations by predicting pixel values of masked video patches.

A novel video diffusion model capable of generating high-quality videos from any number of context frames

VideoMAE is a video self-supervised pre-training model based on Masked Autoencoder (MAE), fine-tuned on the Kinetics-400 dataset for video classification tasks.

A high-fidelity video generation model developed by Genmo, featuring exceptional motion expressiveness and precise prompt adherence

Dynamic LoRAs can add specific types of motion effects to animations

This is a reinforcement learning agent based on the PPO algorithm, specifically designed for training and gameplay in the SpaceInvadersNoFrameskip-v4 game environment.

Stable Video Diffusion (SVD) 1.1 is a diffusion model-based image-to-video tool capable of generating short video clips from static images as conditional frames.

VideoMAEv2-Large is a large-scale video feature extraction model pre-trained with self-supervision on the UnlabeldHybrid-1M dataset

Motion LoRAs can add specific types of motion effects to your animations

Dynamic LoRAs model for adding specific types of motion effects to text-to-video animations

Wan2.1-FLF2V-14B-720P is a video generation model that supports generating videos from images, suitable for various video creation scenarios.

The dynamic LoRA model can add specific types of motion effects to animations, such as zoom in/out, panning, tilting, and rotation.

VideoMAE is a self-supervised video pre-training model based on masked autoencoder, fine-tuned on the Kinetics-400 dataset for video classification tasks.

TimeSformer is a video classification model pretrained on the Kinetics-600 dataset, utilizing a spatiotemporal attention mechanism to process video data.

VideoMAEv2-Base is a self-supervised video feature extraction model that employs a dual masking mechanism pre-trained on the UnlabeldHybrid-1M dataset.

MoviiGen1.1 is a video generation model based on GGUF format conversion, supporting text-to-video tasks.

VideoMAE is a video self-supervised pre-training model based on Masked Autoencoder (MAE), which learns video representations by predicting pixel values of masked video patches

VideoMAE is a video pretraining model based on Masked Autoencoder (MAE), fine-tuned on the Kinetics-400 dataset through self-supervised learning, suitable for video classification tasks.

TimeSformer is a video classification model based on spatio-temporal attention mechanisms, specifically designed for video understanding tasks.

LivePortrait is an efficient portrait animation generation model that achieves static image to dynamic video conversion through stitching and redirection control technology

VideoMAE is a masked autoencoder model for video, pretrained with self-supervision and fine-tuned on the Kinetics-400 dataset, suitable for video classification tasks.

A LoRA trained on the Wan2.1 14B I2V 480p model, capable of transforming any object in an image into a cake-effect video

A video action recognition model based on the ViViT architecture, fine-tuned specifically for CCTV surveillance scenarios, excelling in action recognition tasks.

A LoRA trained based on the Wan2.1 14B I2V 480p model, which can convert static images into dynamic videos with an inflatable effect.

AnimateDiff motion adapter model for adding specific motion effects to generated animations

Dynamic LoRAs can add specific types of motion effects to animations, such as zoom in/out, panning, tilting, and rotation.

An image-to-video model developed by Tencent Hunyuan Community, capable of converting input text descriptions into dynamic video content.

VideoMAEv2-Huge is a self-supervised learning-based video feature extraction model, pre-trained for 1200 epochs on the UnlabeledHybrid-1M dataset.

Dynamic LoRAs model for adding specific types of motion effects to text-generated animations

VideoMAEv2-giant is an ultra-large-scale video classification model based on self-supervised learning, employing a dual masking strategy for pretraining.

ViViT is an extension of the Vision Transformer (ViT) for video processing, primarily used for downstream tasks such as video classification.

VideoMAE is a video self-supervised pretraining model based on Masked Autoencoder (MAE), fine-tuned on the Something-Something-v2 dataset for video classification tasks.

SkyReels-V2-I2V-14B-540P is a GGUF-format converted image-to-video model that supports generating dynamic video content from static images.

VideoMAE is a video self-supervised pretraining model based on Masked Autoencoder (MAE), which learns internal video representations through masked patch prediction, suitable for downstream tasks like video classification.

AnimateDiff is a technology that leverages existing Stable Diffusion text-to-image models to create videos by inserting motion module layers to achieve coherent motion between image frames.

SkyReels-V2-I2V-14B-720P is an image-to-video generation model capable of converting static images into dynamic videos.

LoRA trained on the Wan2.1 14B I2V 480p model for generating kissing interaction videos from images

A latent diffusion model for generating short video clips from static images, supporting 25-frame video generation at 1024x576 resolution

AnimateDiff is a technology that enables existing Stable Diffusion text-to-image models to generate videos by inserting motion module layers to achieve coherent motion between frames.