Video processing with temporal consistency

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CoDeF Overview

TLDR: CoDeF, or Content Deformation Fields, is an innovative AI tool designed for advanced video processing tasks. It offers a unique approach to video manipulation, allowing users to achieve realistic video style editing, segmentation-based tracking, and video super-resolution effortlessly. We give an App Score of 8/10 to CoDeF.

CoDeF stands out for its ability to maintain high temporal consistency, a common challenge in video processing. One of its key features is the combination of a 3D temporal deformation field with a 2D hash-based picture field, enabling precise control over video content. The tool leverages multi-resolution hash encoding to express temporal deformation, making it particularly effective for tracking complex objects like water and smog.

However, a potential drawback is the computational resources required for some tasks, which may limit its accessibility for users with less powerful hardware. CoDeF’s codebase and project can be found on GitHub, providing transparency and opportunities for community contributions and enhancements.

In Development

CoDeF Pros and Cons

Pros Icon
  • High Temporal Consistency
  • Versatile Applications
  • Innovative Approach
  • Open Source
  • Improved Semantic Information
Cons Icon
  • Limited pretrained models
  • Limitation of accessibility with less powerful hardware

CoDeF Pricing

Pricing Models:

✓ Free to use
✓ Open Source

CoDeF Features

  • AI Video Creation
  • Synthesized Video Conversion
  • Video denoising

CoDeF Alternatives

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CoDeF Specifications


  • Linux
  • Mac
  • Windows

Customer Type

  • Freelancers
  • Large Enterprises
  • Medium Business
  • Small Business
  • Solopreneurs


  • English

CoDeF Support


CoDeF, or Content Deformation Fields, is an AI tool designed for video processing. It combines a 3D temporal deformation field with a 2D hash-based picture field to enable tasks like video style editing, segmentation-based tracking, and video super-resolution.

CoDeF requires Ubuntu 20.04, Python 3.10, PyTorch 2.0.0, PyTorch Lightning 2.0.2, and at least one NVIDIA GPU with CUDA version 11.7. A GPU with 10GB memory is sufficient for running the code.

You can download pretrained models for specific video sequences from the provided links in the documentation. These models are organized by sequence name and experiment name within the ckpts directory.

You can segment video sequences using SAM-Track and obtain mask files. Place these masks in the appropriate folder structure within the all_sequences directory. Then, extract optical flows using RAFT and place the pretrained model in the designated folder.

While specific community forums weren’t mentioned, CoDeF is available on GitHub, which allows for collaboration, issue tracking, and discussions related to the tool. This can be a valuable resource for users seeking help or sharing insights.

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