# MiniMax-Remover: Taming Bad Noise Helps Video Object Removal

arXiv: https://arxiv.org/abs/2505.24873 · HTML: https://mlsanity.com/paper/2505.24873/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 47 of its 57 references that are in the map.
- First posted on arXiv 30 May 2025.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 30 of 47)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing](https://mlsanity.com/paper/2306.10012/) (2023)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [VideoComposer: Compositional Video Synthesis with Motion Controllability](https://mlsanity.com/paper/2306.02018/) (2023)
- [TokenFlow: Consistent Diffusion Features for Consistent Video Editing](https://mlsanity.com/paper/2307.10373/) (2023)
- [Step1X-Edit: A Practical Framework for General Image Editing](https://mlsanity.com/paper/2504.17761/) (2025)
- [Video-P2P: Video Editing with Cross-attention Control](https://mlsanity.com/paper/2303.04761/) (2023)
- [Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting](https://mlsanity.com/paper/2212.06909/) (2022)
- [CogVLM2: Visual Language Models for Image and Video Understanding](https://mlsanity.com/paper/2408.16500/) (2024)
- [ProPainter: Improving Propagation and Transformer for Video Inpainting](https://mlsanity.com/paper/2309.03897/) (2023)
- [A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting](https://mlsanity.com/paper/2312.03594/) (2023)

## Built on by (most cited first, 30 of 45)

- [OpenVE-3M: A Large-Scale High-Quality Dataset for Instruction-Guided Video Editing](https://mlsanity.com/paper/2512.07826/) (2025)
- [InstructX: Towards Unified Visual Editing with MLLM Guidance](https://mlsanity.com/paper/2510.08485/) (2025)
- [EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing](https://mlsanity.com/paper/2603.19224/) (2026)
- [EasyV2V: A High-quality Instruction-based Video Editing Framework](https://mlsanity.com/paper/2512.16920/) (2025)
- [O-DisCo-Edit: Object Distortion Control for Unified Realistic Video Editing](https://mlsanity.com/paper/2509.01596/) (2025)
- [OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion Transformer Models](https://mlsanity.com/paper/2509.17627/) (2025)
- [H2R-Grounder: A Paired-Data-Free Paradigm for Translating Human Interaction Videos into Physically Grounded Robot Videos](https://mlsanity.com/paper/2512.09406/) (2025)
- [VOID: Video Object and Interaction Deletion](https://mlsanity.com/paper/2604.02296/) (2026)
- [VideoCoF: Unified Video Editing with Temporal Reasoner](https://mlsanity.com/paper/2512.07469/) (2025)
- [Tele-Omni: a Unified Multimodal Framework for Video Generation and Editing](https://mlsanity.com/paper/2602.09609/) (2026)
- [SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing](https://mlsanity.com/paper/2603.19228/) (2026)
- [Goku: A Million-Scale Universal Dataset and Benchmark for Instruction-Based Video Editing](https://mlsanity.com/paper/2606.30599/) (2026)
- [EraserDiT: Fast Video Inpainting with Diffusion Transformer Model](https://mlsanity.com/paper/2506.12853/) (2025)
- [LoVoRA: Text-guided and Mask-free Video Object Removal and Addition with Learnable Object-aware Localization](https://mlsanity.com/paper/2512.02933/) (2025)
- [From Ideal to Real: Stable Video Object Removal under Imperfect Conditions](https://mlsanity.com/paper/2603.09283/) (2026)
- [NOVA: Sparse Control, Dense Synthesis for Pair-Free Video Editing](https://mlsanity.com/paper/2603.02802/) (2026)
- [LIVEditor-14B: Lightning Unified Video Editing via In-Context Sparse Attention](https://mlsanity.com/paper/2605.04569/) (2026)
- [Ctrl&Shift: High-Quality Geometry-Aware Object Manipulation in Visual Generation](https://mlsanity.com/paper/2602.11440/) (2026)
- [From Understanding to Erasing: Towards Complete and Stable Video Object Removal](https://mlsanity.com/paper/2604.01693/) (2026)
- [Smart-Insertion-V: Photorealistic Video Insertion via a Closed-Loop Feedback Dual-Stream Framework](https://mlsanity.com/paper/2605.23891/) (2026)
- [Robot-Factored World Models via Robot Rendering](https://mlsanity.com/paper/2607.22535/) (2026)
- [ViFeEdit: A Video-Free Tuner of Your Video Diffusion Transformer](https://mlsanity.com/paper/2603.15478/) (2026)
- [CLEAR: Context-Aware Learning with End-to-End Mask-Free Inference for Adaptive Video Subtitle Removal](https://mlsanity.com/paper/2603.21901/) (2026)
- [TAG: Target-Agnostic Guidance for Stable Object-Centric Inference in Vision-Language-Action Models](https://mlsanity.com/paper/2603.24584/) (2026)
- [YOSE: You Only Select Essential Tokens for Efficient DiT-based Video Object Removal](https://mlsanity.com/paper/2604.27322/) (2026)
- [PROVE: A Perceptual RemOVal cohErence Benchmark for Visual Media](https://mlsanity.com/paper/2605.14534/) (2026)
- [JAVEDIT: Joint Audio-Visual Instruction-Guided Video Editing with Agentic Data Curation](https://mlsanity.com/paper/2606.03168/) (2026)
- [Refaccade: Editing Object with Given Reference Texture](https://mlsanity.com/paper/2512.04534/) (2025)
- [EasyOmnimatte: Taming Pretrained Inpainting Diffusion Models for End-to-End Video Layered Decomposition](https://mlsanity.com/paper/2512.21865/) (2025)
- [Learning Stochastic Bridges for Video Object Removal via Video-to-Video Translation](https://mlsanity.com/paper/2601.12066/) (2026)

## Abstract (arXiv)

Recent advances in video diffusion models have driven rapid progress in video editing techniques. However, video object removal, a critical subtask of video editing, remains challenging due to issues such as hallucinated objects and visual artifacts. Furthermore, existing methods often rely on computationally expensive sampling procedures and classifier-free guidance (CFG), resulting in slow inference. To address these limitations, we propose MiniMax-Remover, a novel two-stage video object removal approach. Motivated by the observation that text condition is not best suited for this task, we simplify the pretrained video generation model by removing textual input and cross-attention layers, resulting in a more lightweight and efficient model architecture in the first stage. In the second stage, we distilled our remover on successful videos produced by the stage-1 model and curated by human annotators, using a minimax optimization strategy to further improve editing quality and inference speed. Specifically, the inner maximization identifies adversarial input noise ("bad noise") that makes failure removals, while the outer minimization step trains the model to generate high-quality removal results even under such challenging conditions. As a result, our method achieves a state-of-the-art video object removal results with as few as 6 sampling steps and doesn't rely on CFG, significantly improving inference efficiency. Extensive experiments demonstrate the effectiveness and superiority of MiniMax-Remover compared to existing methods. Codes and Videos are available at: https://minimax-remover.github.io.