Search Results for "video frames"
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Showing 151 open source projects for "video frames"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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LongCat-Video
Foundational video generation model with 13.6B parameters
LongCat-Video is a 13.6-billion-parameter foundation model for generating and extending video. A unified architecture handles text-to-video, image-to-video, and video-continuation tasks without separate models. It is pretrained for continuation, allowing it to create minutes-long sequences while limiting color drift and quality loss. A coarse-to-fine strategy operates across time and space to produce 720p video at 30 frames per second efficiently. ... -
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claude-video
Give Claude the ability to watch any video
Claude Video is an agent skill that gives Claude and compatible coding assistants the ability to analyze video content. It accepts public video URLs or local video files, then extracts the information needed to answer user questions about what happened on screen and in the audio. The workflow checks captions first, downloads only what is necessary, extracts timestamped frames, and produces a transcript through native captions or Whisper fallback. ... -
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Step-Video-T2V
State-of-the-art (SoTA) text-to-video pre-trained model
Step-Video-T2V is a state-of-the-art text-to-video foundation model developed to generate videos from natural-language prompts; its 30B-parameter architecture is designed to produce coherent, temporally extended video sequences — up to around 204 frames — based on input text. Under the hood it uses a compressed latent representation (a Video-VAE) to reduce spatial and temporal redundancy, and a denoising diffusion (or similar) process over that latent space to generate smooth, plausible motion and visuals. ... -
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Frame
Fast FFmpeg GUI
Frame is a multimedia-focused project designed to handle video frame extraction, analysis, and processing workflows using FFmpeg-based pipelines. It enables developers to work directly with individual frames from video streams, supporting tasks such as frame sampling, transformation, and analysis. The tool is structured to facilitate automation, allowing users to process frames in bulk or integrate frame-level operations into larger media pipelines. -
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Claude Code Video Vision
Give Claude the ability to watch and understand videos
Claude Video Vision is a plugin designed for Claude Code that enables large language models to process and understand video content by transforming it into multimodal inputs the model can reason over. Instead of attempting to directly interpret raw video streams, the system extracts key frames using tools like ffmpeg and processes audio through transcription engines, converting both visual and auditory signals into structured inputs for the model. -
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Olive Video Editor
Free open-source non-linear video editor
0.2 is the upcoming major release of Olive. It's a complete rewrite from the ground up designed around cutting-edge features to help you make the best videos possible. Olive 0.2 provides powerful and flexible node-based compositing. Node editing is a form of visual programming that gives you full control over how Olive renders your video. Rather than a "fixed" pipeline where one effect occurs after the other, nodes allow you to connect anything to anything else allowing a ton of flexibility... -
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claude-real-video
Let Claude (or any LLM) actually watch a video
claude-real-video is a local video processing tool that prepares videos for analysis by Claude or other LLMs. It accepts public video URLs or local files and extracts the visual and audio evidence an AI model needs. Instead of sampling at a fixed interval, it detects scene changes and removes near-duplicate frames to reduce unnecessary token usage. -
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FramePack
Lets make video diffusion practical
FramePack explores compact representations for sequences of image frames, targeting tasks where many near-duplicate frames carry redundant information. The idea is to “pack” frames by detecting shared structure and storing differences efficiently, which can accelerate training or inference on video-like data. By reducing I/O and memory bandwidth, datasets become lighter to load while models still see the essential temporal variation. -
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JoyAI-Video-Edit
Real-Time Open-Ended Video Editing with Autoregressive Diffusion
JoyAI-Video-Edit is a real-time, instruction-guided system for editing open-ended video streams as frames arrive. It works with live camera feeds or uploaded videos without waiting for the entire sequence or knowing the final video length. Natural-language instructions can drive subject edits, local changes, background replacement, style transfer, motion changes, and reference-guided transformations. -
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LatentSync
Taming Stable Diffusion for Lip Sync
LatentSync is an open-source framework from ByteDance that produces high-quality lip-synchronization for video by using an audio-conditioned latent diffusion model, bypassing traditional intermediate motion representations. In effect, given a source video (with masked or reference frames) and an audio track, LatentSync directly generates frames whose lip motions and expressions align with the audio, producing convincing talking-head or animated lip-sync output. ... -
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HyperFrames
Write HTML. Render video. Built for agents
HyperFrames is a framework developed by HeyGen that focuses on generating and managing structured, dynamic content experiences powered by AI. It introduces the concept of “frames,” which represent modular units of content that can be dynamically composed and rendered based on context or user interaction. The system is designed to enable highly interactive and personalized experiences, particularly in applications such as video generation, storytelling, and user interfaces. It supports integration with AI models to generate or modify content within these frames, allowing real-time adaptation. ... -
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Deep-Live-Cam
Real time face swap and one-click video deepfake
Real time face swap and one-click video deepfake with only a single image. Choose a face (image with the desired face) and the target image/video (image/video in which you want to replace the face) and click on Start. Open File Explorer and navigate to the directory you select your output to be in. You will find a directory named <video_title> where you can see the frames being swapped in real time. -
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OpenHiggsfield AI
A studio for image and video generation
OpenHiggsfield is an open-source browser studio for generating images and videos through multiple AI models from one interface. A single prompt composer handles both media types, while the selected model determines which generation workflow is used. Its catalog includes dozens of image and video models from several providers. Each model exposes only the settings it supports, such as aspect ratio, resolution, duration, audio, batch size, and prompt enhancement. Users can attach uploads or reuse media from previous generations as references, start frames, end frames, video, or audio inputs. Completed generations are organized into a shared history and asset library. ... -
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SlowFast
Video understanding codebase from FAIR for reproducing video models
SlowFast is a video understanding framework that captures both spatial semantics and temporal dynamics efficiently by processing video frames at two different temporal resolutions. The slow pathway encodes semantic context by sampling frames sparsely, while the fast pathway captures motion and fine temporal cues by operating on densely sampled frames with fewer channels. -
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Blur
Add motion blur to videos
Blur is a command-line tool designed to simplify video processing workflows focused on blurring and censoring sensitive regions within video content. It provides a streamlined interface for applying blur effects to specific areas, often using FFmpeg as the underlying processing engine. The tool enables automation of repetitive editing tasks such as anonymizing faces, text, or objects in video frames. -
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StoryGen Atelier
AI-assisted storyboard and video generation tool
...Users begin with natural language descriptions of their story or scene, and the system uses state-of-the-art large models to generate both the script and corresponding frames. Once individual frames are created, a second AI model generates transition clips that smoothly link the frames into a coherent short video sequence, and the tool then assembles everything into a finished video using standard video processing tools. This pipeline transforms abstract creative ideas into shareable visual narratives, offering a powerful workflow for writers, filmmakers, content creators, and educators alike. -
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MiniMax H3
MiniMax H3 is a general-purpose, omni-modal generative system
MiniMax H3 is an omni-modal generative system for creating synchronized video and native stereo audio from complex multimodal instructions. It can understand combinations of text, images, video, and audio as generation context. Outputs range from four to fifteen seconds at 24 frames per second, with multiple aspect ratios and resolutions up to 2K. H3-Base supports text-to-video, first-frame, last-frame, and first-and-last-frame generation. -
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Remotion
Make videos programmatically with React
Remotion is a cutting-edge library that lets developers create real videos programmatically using React components, transforming familiar UI paradigms into a flexible, code-driven video production workflow. Instead of traditional timeline editors, Remotion leverages HTML, CSS, and JavaScript to define video frames, animations, and transitions, which means developers can use states, props, loops, and component hierarchies to automate complex motion graphics. Because it integrates with the React ecosystem, Remotion fits naturally into modern front-end stacks and tooling, and can produce dynamic content like personalized videos, dashboards, and data-driven animations with the same code used to build interactive web apps. ... -
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Practical-RIFE
More practical frame interpolation approach
Practical-RIFE is an open-source video frame interpolation project that improves the usability and visual quality of the RIFE deep learning architecture. It generates intermediate frames between existing video frames to produce smoother motion and higher frame rates. The project provides multiple pretrained models optimized for different content types, including animation and AI-generated video. -
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Memvid
Video-based AI memory library. Store millions of text chunks in MP4
Memvid encodes text chunks as QR codes within MP4 frames to build a portable “video memory” for AI systems. This innovative approach uses standard video containers and offers millisecond-level semantic search across large corpora with dramatically less storage than vector DBs. It's self-contained—no DB needed—and supports features like PDF indexing, chat integration, and cloud dashboards. -
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Clypra
Hardware-accelerated video editor built on Rust, Tauri v2 & React 19
Clypra is an open-source desktop video editor for macOS, Windows, and Linux, with a core released under the MIT License. Its multitrack timeline supports frame-accurate trimming, layered arrangements, and undo and redo controls. A native Rust engine combines FFmpeg processing with GPU acceleration for video decoding and rendering. Audio tools include waveform visualization, synchronized audio and video playback, and individual clip volume controls. Users can add styled text, animated... -
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LTX-2.3
Official Python inference and LoRA trainer package
LTX-2.3 is an open-source multimodal artificial intelligence foundation model developed by Lightricks for generating synchronized video and audio from prompts or other inputs. Unlike most earlier video generation systems that only produced silent clips, LTX-2 combines video and audio generation in a unified architecture capable of producing coherent audiovisual scenes. The model uses a diffusion-transformer-based architecture designed to generate high-fidelity visual frames while simultaneously producing corresponding audio elements such as speech, music, ambient sound, or effects. ... -
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FFmpeg Sidecar
Wrap a standalone FFmpeg binary in an intuitive Iterator interface
FFmpeg Sidecar is a Rust library that wraps a standalone FFmpeg binary and exposes it through an intuitive, iterator-based interface. It allows developers to interact with video streams as sequences of raw frames, abstracting the complexity of decoding and processing media. The library leverages FFmpeg’s CLI instead of low-level bindings, simplifying setup and avoiding heavy dependencies. It enables streaming data through stdin and stdout, making it suitable for real-time processing pipelines. Additionally, it parses FFmpeg logs to extract structured information such as progress, metadata, and errors. ... -
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PyAV
Pythonic bindings for FFmpeg's libraries
...While powerful, it requires a solid understanding of FFmpeg concepts, as it prioritizes flexibility and control over abstraction. Overall, PyAV is a robust tool for developers building advanced video and audio processing systems in Python. -
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CVPR2023-DMVFN
Dynamic Multi-Scale Voxel Flow Network for Video Prediction
DMVFN is an open-source PyTorch implementation of the Dynamic Multi-Scale Voxel Flow Network for video frame prediction, presented at CVPR 2023. It predicts future video frames by analyzing motion and visual information from preceding frames. The architecture uses dynamic multi-scale voxel flow to estimate motion across different spatial resolutions. This approach helps model complex movements while managing computational requirements.
