MiniMax-Remover - An AI-powered method for removing targets from videos, achieving high-quality removal results.

What is MiniMax-Remover?

MiniMax-Remover is a novel video object removal method that addresses common issues in existing technologies, such as illusory objects, visual artifacts, and slow inference speed. It employs a two-stage approach: the first stage is based on a simplified DiT architecture, removing text input and cross-attention layers to obtain a lighter and more efficient model; the second stage distills the model using a minimax optimization strategy, identifying adversarial input noise and training the model to generate high-quality results under these conditions. Requiring only 6 sampling steps and independent of classifier-free guided (CFG), it achieves advanced video object removal results and significantly improves inference efficiency.

MiniMax-Remover's main functions

• Highly efficient video target removalThe method employs a two-stage approach. The first stage is based on a simplified DiT architecture, removing text input and cross-attention layers to obtain a lighter and more efficient model architecture. The second stage uses a min-max optimization strategy to distill the remover, further improving editing quality and inference speed.
• Fast reasoning speedIt requires only 6 sampling steps and does not rely on classifier free guidance (CFG), which can achieve advanced video target removal effects and significantly improve inference efficiency.
• High-quality removal effectThe model identifies adversarial input noise through an internal maximization step and trains the model under these conditions through an external minimization step, thus avoiding problems such as illusory objects and visual artifacts.

MiniMax-Remover's technical principles

• Phase 1 Model Architecture OptimizationMiniMax-Remover first adopts a simplified version of the DiT (Diffusion in Time) architecture, removing text input and cross-attention layers to obtain a lighter and more efficient model architecture. The goal of this stage is to reduce model complexity, improve inference speed, and maintain basic video object removal capabilities.
• Second stage: Minimum-Maximum OptimizationBuilding upon the first stage, MiniMax-Remover further distills the model using a min-max optimization strategy to improve editing quality and inference speed. The specific steps are as follows:
  • Internal maximizationIt identifies adversarial input noise (“bad noise”) that could lead to removal failure. The noise simulates the worst-case scenarios that might be encountered in real-world applications, helping the model learn how to cope with these challenges.
  • External minimizationThe trained model can generate high-quality removal results even under these adversarial conditions. In this way, the model can maintain good performance even in the worst case.

MiniMax-Remover project address

• Project official website:https://minimax-remover.github.io/

Application scenarios of MiniMax-Remover

• Film and television post-productionIn the post-production of films, TV series, commercials and other film and television works, it is necessary to remove some unwanted elements, such as props that make mistakes, redundant actors, logos, etc. MiniMax-Remover can complete these tasks quickly and with high quality, saving post-production time and costs.
• Video content creationFor self-media video creators, MiniMax-Remover can help them easily remove distracting elements from their videos, such as irrelevant people or billboards in the background, making the video content more concise and professional, and improving the viewing experience for the audience.
• Video repair and optimizationIn the restoration of some old or damaged videos, MiniMax-Remover can be used to remove blemishes and stains from the video, restoring its clarity and integrity.
• Video effects productionWhen creating video effects, MiniMax-Remover can be used as a pre-processing tool to remove original elements from the video, providing a clean background for subsequent effects.