MiniMax-Remover: Taming Bad Noise Helps Video Object Removal
Authors: Bojia Zi
Weixuan Peng
Xianbiao Qi
Jianan Wang
Shihao Zhao
Rong Xiao
Kam-Fai Wong
Venue: NeurIPS 2025 poster ()
Keywords: Video Object Removal
Primary area: applications

Abstract: 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. In this way, we obtain a more lightweight and efficient model architecture in the first stage. 
In the second stage, we proposed a minimax optimization strategy to further distill the remover with the successful videos produced by stage-1 model. Specifically, the inner maximization identifies adversarial input noise ("bad noise'') that leads to failure removals, while the outer minimization 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 using as few as 6 sampling steps without CFG usage. 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**.

MiniMax-Remover: Taming Bad Noise Helps Video Object Removal
Bojia Zi Weixuan Peng?.'
Xianbiao Qi Jianan Wang",
Shihao Zhao Rong Xiao", Kam-Fai Wong
'The Chinese University of Hong Kong
Shenzhen University
IntteliFuusion Inc.
Astribot Inc.
"The University of Hong Kong
MMo Key Laboratory of High Confidence Software Technologies, CUHK
Figure 1: Visual Results of MiniMax-Remover. The left side displays the original videos, while the right side shows the edited results. Our method achieves high-quality removal of the target objects: the girl, chameleon, bird, lane line, and red wine glass, illustrated in the five corresponding video examples. Best viewed with Acrobat Reader. Click the images to play the animations.
Abstract
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 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--Remveer, novel two-stage
Equal Contribution.
Corresponding author. Email: eerianbiao@ggail.coo
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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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. In this way, we obtain a more lightweight and efficient model architecture in the first stage. In the second stage, we proposed minimax optimization strategy to further distill the remover with the successful videos produced by stage-1 model. Specifically, the inner maximization identifies adversarial input noise ("bad noise") that leads to failure removals, while the outer minimization trains the model to generate high-quality removal results even under such challenging conditions. As result, our method achieves a state-of-the-ee art video object removal results using as few as sampling steps without CFG usage. Extensive experiments demonstrate the effectivenesss and superiority of MiniMax-Remover compared to existing methods. Codes and Videos are available at: https: hotpss///miiiaxx... removarr baaovverrgggtutb github.
Introduction
In recent years, diffusion models [ 16 8 11 37 33 44 57 have driven remarkable progress in image and video generation. UNer-based architectures, exemplified by models such as
AnimateDiff [T6] and VideoCrafter2 (8) have demonstrated impressive capabilities in generating high-quality videos. More recently, the field has advanced with the introduction of transformer- based architectures, as seen in groundbreaking works such as Sora [37]. Hunyuan Video [22] and 1an2.1 [4), which employ DiT-based structures (38] achieve unprecedented generation quality.
Parallel to these advancements, diffusion-basee editing techniques (6 52 47 12 30 48 2 20 58
28
49
13
10 23 have also seen significant progress. Among these, video inpainting has emerged
as particularly crucial capabillit,, serving as foundational component for numerous fine-grained video editing applications.
Video object removal focuses on erasing specified objects from videos while preserving background consistency and temporal coherence. ProPainter [56] addressed this by completing optical flow within masked regions and then synthesizing the fill-in content using vision transformers, rather than relying on generative models. More recently. the field has shifted toward diffusion- bassdd methods, with several notable advancements. FFF-VDI 24 uses optical flow and generative model to inpaint videos. DiffuEraser (25] employs DDIM inversion and vision priors to enhance video inpainting quality. Similarly, FloED [5| proposes a bual-branch architecture and a two-stage training strategy to achieve higher fidelity in the regions where objects are intended to be removed.
Despite rapid progress, existing video object removal methods still face multiple challenges. Some methods generate undesired content or artifacts, while others introduce occlusions and blurs within masked regions. Furthermore, most approaches rely heavily on auxiliary priors (e.g., optical flow, text prompts, or DDIM inversion). The complicated model design lead to unsuitability and limit frinddisssl. Moreover, the generative methods require large number of sampling steps to achieve high visual fidelity and depend on classifier-free guidance (CFG), which requires two model evaluations per sampling step, further exacerbates inference latency, limiting their practicality in real-world applications.
To address these limitations, we propose a two-stage framework for video object removal that simultaneously enhances visual quality and reduces inference cost. In Stage 1, we train upon 1.13B 44) and introduce two key modifications: (i) replacing external text prompts with learnable contrastive condition tokens, and (ii) removing all cross-attention layers within the DiT blocks, instead injecting contrastive condition tokens directly into the self-attention stream to enable conditional control. This lightweight conditioning approach significantly reduces computational overhead while preserving the structural integrity of the DiT backbone, allowing the same weights to be reused in the next stage, even when conditional control disabled. In Stage 2, we distill the remover on manually curate 10K high-qualin object removal samples generated by the model in Stage I and introduce a minimax optimization strategy further improve object removal performance. The inner maximization searches for adversarial noise ("bad" noise) that induces model failure, while the outer minimization trains the model to be robust against such challenging cases. This minmax optimization enables our remover to consistently produce artifact-free results and prevents generating undesired objects without the assistance of CFG. As a result, the final model achieves both superior visual quality and remarkable inference efficiency compared to existing methods.

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Table I: Comparison of our MiniMax-Remone and other Video Inpainting Methods. We compare these methods on a video with 33 frames and 360P resolution on A800 GPUs, using their default configuration. "-" means unknown, while "N/A" means not applicable. Senorita-R R means Senorita- 2M remover expert. DiffuEraser uses ProPainter to generate prior (1 step) and PCM [45] to refine the edited output (2 steps). The motion adapter (20 steps) and DDIM inversion in DiffuBraser are both optional.
-Inference Details
Additional Condition / Prior
Open- Source
Method
Params
Min Steps
CPG
Latency
GPU Mem (GB)
DDIM
Optical Flow
Text
First Frame
(6)
Inversion
Prompe
AVID 55]
1,95B
FFT-VDI
24
50
X
X
VIVID 20
5,57B
Senorita-R
58
1.69B
MTV-Inpaint
80
1.41B
30
X
X
X
X
Propainter
56
39.4M
NIA
N/A
0.20
12.1
V
VidroComposer
48
1.40B
V
2.83
49.1
COCOCO 59
1.55B
3.56
36.5
HoED 15
1.418
25
1.32
47.6
DiffuEraser 25
2,19B
10.4
VideoPuinter
2
5.92B
8.14
44.7
VACE 21
1,78B
25
1.93
23.6
SiniMax-Removee
1.12B
X
0.18
8.2
Our main contributions are summarized as follows:
1. propose lightweight yet effective DiT-based architecture for video object removal. Motivated by the observation that text prompting is not best suited for the task of object removal, we replace the text conditions with learnable contrastive tokens control the removal process. These tokens are integrated directly into the self-attention stream, allowing us to remove all cross-attention layers within the pretrained video generation model. As result, in Stage 1, our model features fewer parameters and no longer depends on ambiguous text instruction.
In Stage 2, we distilled remover on 10K manually selected video removals produced by the model in Stage 1, with minmax optimization strategy. Specifically, the inner maximization optimization finds a bad input noise that makes model fail, while the outer minimization optimization tries to teach model remove the bad noise added on the successful videos.
3. We conduct extensive experiments across multiple benchmarks, demonstrating that our method achieves superior performance in both inference speed and visual fidelity. As shown in Figure and Table our model produces high-quality removal results using as few as 6 sampling steps and without relying on classifier-fre guidance (CFG).
2 Related Work
Video diffusion models have witnessed remarkable progress in recent years 48 46 16 g 37
33
accompanied by the emergence of various video editing techniques 27
26
10
58) Among these techniques, video inpainting plays crucial role by enab enabling
the regeneration content in corrupted or missing regions 48 59
58 20
55 56
15 25
58 24
Generally, video inpainting tasks can be categorized into two main types: one focuses on generating or editing objects within specified regions with prompts, while the other aims to remove objects based on provided masks.
Text-guided Video Inpainting. For the first type of video inpainting, which involves generating or editing content within a masked region, significant progress has been made in recent years. VideoComposer 48 is the first diffusion model capable of performing text-guided video inpainting. It supports multiple conditioning inputs and can handle various tasks within a unified framework. AVID (55) proposed method for text- exxt-guided video inpainting of arbitrary- sequences using natural language prompts. COCOCO (59] improves consistency and controllability through damped global attention and enhanced cross-attention to text. VIVID 20 introduces a large-scale dataset with 10M image and video samples for localized editing, enabling training of powerful text-guided inpainter. MTV--Inpaint [50] capable of handling both traditional scene completion and novel
object insertion. VideoPainter [2] performs inpainting using DiT-based architecture, incorporating
3

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an efficient context encoder to process masked inputs and injecting backbone-awan background information into a pre-trained video DřT for plug-and andg-and-pe play consistent video inpainting.
Video Object Removal, In contrast to text-guided inpainting, some methods focus specifically on removing objects from videos. ProPainter [56] uses optical flow to first complete the motion in masked regions, then synthesizes inpainted content accordingly. FFF-VDI [24] propagates noise latents from future frames to fill masked areas in the initial latent space, then finetunes pre-trained image-to-video diffusion model for final synthesis. FloED [5] integrates both optical flow and text prompts, injecting embeddings from both modalities into the impainting model for object removal. DiffuEraser [25] combines a flow-guide dlodddddedd inpainting model with DDIM inversion for improved inpainting fidelity. Senorita- Remover, proposed (58), introduces instruction-bic bassed object removal, using positive prompts to guide removal and negative prompts to suppress unintended object generation in masked areas.
Remark. Compared with previous methods, our MiniMax-Remonee does not rely on additional prior or prompts. It offers fast inference speed with only sampling steps, no CFG. Despite its simplicity, achieves higher object removal success rate and prevent hallucinated object and artifacts generation.
3
Preliminary
Flow Matching, Given an input æ E Rfxxxxxe, pretrained VAE encoder maps it to a latent code
Zu E(zu) R xixx x xhi xei The model input is noisy latent 2,, where 2; = èe + (1 t) where e ~ NN, I) and E [0, 1]. t is usually sampled from a logit-normal distribution. The target velocity is 2u- The optimization process of Flow Matching is defined as,
0¹ argmin F2,,, | () 21
0
denotes the model parameterized by 0. The model can be DiT
38 or UNet
43 architecture.
Diffusion-based Video Inpainting. This approach is reconstruct masked video regions using conditional diffusion model. The denoising network ug takes a noisy latent 2₁, a masked latent zm latent code m for the mask m, and condition e as inputs. It optimizes the following objective.
0 argmin Er,,, [ mm, m, c, Edl12]
Generally, could be optical flow text prompts.
Classifier Free Guidance (CFG). CFG [8] improves conditional generation by training the model with and without condition c. The network jointly learns the following processes,
uz t, c) x - V log Po (z,lc),
uo(zi,t,2) & V. log (zt)
(3)
In the inference stage, it combines the following two modes:
(z,, t) = ui 2t. t, 0) + w (ua(zz,t, up (z (z),
(4)
where w controls the conditioning strength. However, employing CFG at inference time doubles the computational cost, as it requires two forward passes per sampling step.
MiniMax Optimization. widely employed in classical convex optimization 5 robustness enhancement and generative adversarial networks (GANs) [14]. The objective is defined as,
f = argmin max L(f(z, 0).y)
(5)
where L is the loss function, f is the model with parameter 0, z and y are the input and ground truth.
4 Methodology
4.1 Overall Framework
As shown in Figure 3 our method can be summarized as two stages.

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Stage 1: Training a lightweight video object removal model. Our method follows the standard video inpainting pipeline, but with two simple yet effective improvements. First, we design a lightweight architecture by removing irrelevant components. Unlike many existing methods 59 55
48
25
we do not use text prompts or extra inputs such as optical flow and text prompts, allowing us to remove all cross-attention layers. Second, we introduce two contrastive condition tokens to guide the inpainting process: a positive token, which encourages the model to fill in content within the masked regions, and negative token, which discourages the model from generating unwanted objects in those areas. should be noted that unlike prior works [55 56 not rely additional conditions.
we only use object mask and do
Stage Enhancing model robustness and efficiency via human-guided minimax optimization. We first use the model in Stage 1 to generate inpainted video samples and then ask human annotators to identify successful results. Using this curated subset, we apply a minimax optimization training scheme to enhance the model's robustness and generation quality. Furthermore, the distilled remover can use as few as 6 steps without the help of CFG, resulting a fast inference. The resulting improved model is referred to as
4.2 Stage 1: A Simple Architecture for Video Object Removal
Our method is built on the pretrained video generation model Wan2.1-1.3B 44 Matching model with DiT architecture.
which is a Flow
4.2.1 Model Architecture
Input Layer. We start by concatenating three types of latents: a noisy latent 21, a masked latent 2mo and mask latent m. They are defined 2, = le (1 =])2.. where E(r), 2m E(m @ a)) and m = {mm). Here, x denotes the input video, E [0, 1] is the diffusion timestep, and the VAE encoder. Each latent has 16 channels, resulting in concatenated input of 48 channels. To accommodate this input, we modify the pretrained patch embedding layer to accept 48 channels instead of the original 16. Specifically, the first 16 channels retain the pretrained weights, while the remaining 32 channels are iert-initialized.
Output
Inpet Tolens Time Fiitted
lapat Tuiano
Taae Endoal
Gentruthvv Cand Tikeer
Iapu: Tokera
Thene Exrood
Reshape
Scale
Dradi Dnib
Scal:
Layer Norm
Key
Scale
Scas
Scal
Croos
KN
DIT Black
Sulee Stife
Slaf
Sunc Sifl
Teleen inieated
Stale chift
Parckity
Embbed
Iuput
WanZ.1 block
block Stage
(c) Stage
Figure 2: The comparison between different blocks. (a) the original Wan2. DiT block; (b) DiT block with contrastive tokens (positive negative token); (c) the block with removing the CFG.
Removing Cross Attention from Pretrained DiT Block. In the pretrained Wan2.1-1.331 44 model, time information is injected via shift table, a bias-based mechanism to encode timestep information. Additionally, the model employs cross-attention module incorporate textual conditioning during video generation. However, for the task of video object removal, textual input is often unnecessary or ambiguous. Therefore. in our model, remove the textual cross-attention layers in the DiT blocks, while retaining the shift table to preserve time information.
Injecting Contrastive Condition Tokens via Self-Attention. To enable conditional inpainting, introduce two learnable condition tokens, denoted as c+ (positive token) and c (negative token), as replacement for text embeddings. We refer to these tokens contrastive condition tokens.
Removing the cross-attention from DiT introduces a challenge: how to effectively inject conditional information without relying textual prompts. A straightforward approach is to repurpose the shift
5

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table to incorporate both timestep and condition information. However, our experiments show that this approach leads to unsatisfactory conditional inpainting results. To achieve more effective conditioning. we instead inject the contrastive condition tokens into the DîT block via the self- self-attention module. Specifically, we employ a learnable embedding layer to project the conditional token into high- dimensional feature, and then split the feature into 6 tokens to increase control ability in attention computation process. These condition tokens are concatenated with the original keys and values the self-attention module, enabling effective conditioning with minimal architectural modifications, For clarity, consider an example: in the original self-attention module, let Q Raxd E Raxd, V Raxd, after injecting the condition tokens, Q E Raxd K E R(++0)xd V E ((++6)xd The updated DiT block after injecting contrastive condition tokens is illustrated in Figure 2
(a) Stage
(b) Stage
Search "bad" noise
Tame "bad" noise
7m
171
Neg. Tolanns
m
Simplified DIT
Pos Tokens
hacloward
c+
Simplified DIT
Simplified DIT
X
Loss
Loss
Zg
Apply CFG
1 Uig (E, Zm 2mm m) V.
Better Quality
2-11
22
WGz 2
de
Final Model
Clear Removal
Sampling
No
v1 de sign(V.)]e1
Prevent Generation
Encourage Removal
Figure 3: The pipeline of our two-stage method.
4.2.2 Contrastive Conditioning for Object Removal
We employ the positive condition token e to guide the remover network in learning object removal and encourage the model to generate target objects under the guidance of c Specifically, when applying classifier-free guidance, e+ serves as the positive condition and c as the negative condition, steering the model away from regenerating the target objects, preventing the reappearance of undesired objects within the masked regions. To train this behavior, we use two complementary strategies: First, training the model remove objects. We randomly select masks from other videos and apply them to the current original video. The original video is used as the ground truth, and the model is conditioned on the positive prompt ct. Since these masks usually don't 't match any real object in the current video, the model learns to fill the masked areas using information from the surroundings, instead of trying to recreate an object that fits the mask shape. This helps the model focus on inpainting the background rather than generating new objects. Second, training the model to generate objects. We use accurate masks that tightly cover real objects in the same video, along with the negative prompt c This teaches the model to connect the shape of the mask with the object should generate. During inference, we can use e as a negative signal to prevent the model from reconstructing the object in those areas. More details are provided in the Appendix.
Given latent features 2,, masked latents zm mask latents m,, and timestep I, the network us predicts:
c ug (Zt; Rms m, ct t),
27 ug (zc, Zm, m. ,)).
(6)
Using CFG with guidance weight w, the final prediction is computed as:
2 أد + w -
The positive token guides the remover network in learning object removal, and the negative token encourages the model to generate object content. We would like to point out that we employ CFG during Stage of training to facilitate conditional learning. However, CFG removed Stage to improve inference efficiency.

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4.2.3 Limitations of Stage
Despite improvements in simplicity and speed, the current model still faces three limitations. (1) CFG doubles the inference time and requires manual tuning of the guidance scale, which can vary across videos. (2) sampling 50 diffusion steps per frame remains time-consuming. (3) Artifacts or undesired object regeneration may occasionally occur within the intended object removal region, indicating that the contrastive signal is not yet fully effective. To address these limitations, we introduce our Stage 2 method, which designed to enhance robustness, quality, and efficiency.
4.3
MiniMax- Distill a Stronger Video Object Remover from Human Feedback
Although our video object remover is trained with contrastive conditioning. it still produces noticeable artifacts and occasionally reconstructs the very object it is supposed erase. Upon closer examination, we observe that these failure cases are strongly correlated with specific input noise pattens. This insight motivates our objective: identify such 'bad noise" and train the object removal model be robust against it.
The minmax optimization also allows to escape from the CFG usage. In Stage 2, climinate CFG to improve sampling efficiency. Specifically, during training, we omit both the positive and the negative condition token. We choose to leave more analysis this design in Appendix.
Moreover, performing inference with 50 steps time-consuming. To address this, we distill the model in Stage using the Rectified Flow method (31 to accelerate the sampling process. To build dataset for minimax optimization, we manually selected 10K video pairs from the 17K generated by the Stage 1 model as training data. This not only reduces the number of sampling steps but, with the help of min-max optimization, also encourages the model to produce better object removal results. Consequently, our model formulation changes from ug (Zt, mm: m, c, to ugg (zt, Zm: m, We formulate our stage-2 training as a minimax optimization problem:
min max I., (2 Zm; m, 1) - 011 where v = 6 ssnce and 2, = le + (1 z)=0.
(8)
0
Therefore, the inner maximization seeks bad noise that maximizes the prediction error, effectively finding challenging input noise. The outer minimization then updates the model parameters O to be robust against such adversarial noise. This minimax optimization strategy encourages the model to remain stable even under difficult or misleading input noise.
4.3.1 Search for "Bad" Noise
One of the key challenges in Equation is how to effectively identify a "bad" noise sample e. Rather than directly maximizing the loss in Equation Jusing successful object removal cases, instead minimize the loss with respect to a bad target: specifically, the original video, which fails to meet the objective of object removal. This leads the following reformulated objective:
min eoc (0.1)
uu = mm, m, = 1.0) o/|?
where v sg(E)
Zorg
where s-(.) is the stop gradient operation and zog denotes the latent code of the original video. It is important to note that, in Equation we omit the expectation R,,,,- because we fix the diffusion timestep to t 1.0, making 21
Starting from a random noise 2 = C, we compute the gradient of the loss function with respect to via backpropagation. This gradient is given by:
V i (c Zm. m, t= 1) -1/))=////// //l
(10)
After obtaining the gradient with respect to the noise, we can construct a new adversarial ("bad"") noise sample e* as follows.
(1))
where c' is a newly sampled noise, sign (V.) the sign of the gradient obtained in Equation 10 and
[0. 1] is randomly sampled scalar. We use only the sign of the gradient to suppress the influence of gradient magnitude, ensuring more stable updates. This resulting noise e" encodes object-related information that tends to reconstruct the original content, thereby serving as challenging adversarial noise. Meanwhile, the formulation in Equation II preserves the approximate distributional properties of standard Gaussian noise, making e" compatible with the diffusion process.
7

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4.3.2 Optimize for Robustness to "Bad" Noise
In Stage 2, we enhance the robustness of the model by fine-tuning it on adversarial noise samples. We minimize the following objective,
min Ex, |lall|/0(22 [ Pm, m, - 001 -000--11 0
where v² = Ssuce and z - Le" + (1 Ssnces (12)
suce is the latent for successful inpainted video. In Equation 12 we sample timestep i, and construct a noisy latent input 2 according to the previously generated E" and sueee In practice, we train mixture of data. Specifically, one-third of training samples are drawn from our curated 10K set with their associated adversarial noises, while the remaining two-thirds are standard Web I0M videos with randomly generated object masks. This mixed strategy ensures the model remains effective on both clean and challenging inputs, ultimately leading to improved generalization and resilience against failure cases. We refer to the model after Stage training MiniMax Remover.
4.3.3 Advantages of MinoMax-Remover
MiniMax-Remover owns several key advantages:
Low Training Overhead. It only back prok-prrpagates once to search the "bad" noise, and trains remover with simplified architectures which reduces the memory consumption.
Fast Inference Speed. MiniMax- Remover only uses as few as sampling steps without CFG, resulting in significantly faster inference compared to prior methods.
High Quality. Since the model is trained to be robust against "bad" noises, it rarely produces unexpected objects or visual artifacts in the masked regions, resulting in a higher quality.
5 Experiments
Training Dataset. In Stage 1, we use Grounded-SAM2 29 42 and captions from Cog [9 to generate masks on the watermark-fre
ViebVid-10M dataset M. Approximately 2.5M video-mask pairs are randomly selected for training. In Stage 2, we collect 17K videos from Pexels [39] and apply the same annotation process as in Stage 1, These are further processed using the model from Stage 1, and 10K videos are manually selected for Stage 2 training.
Newly added layers, such as the embedding layer, are randomly initialized. The first 16 channels of the patch embedder are copied from 1a while the remaining 32 zero-initialized. Training uses batch size of 1 128, input frame length of 81, and resolutions randomly sampled from 336 x 592 to 720 x 1280. We set the first N mask frames to 0 to support the any-length inpainting by applying sliding windows, using a random ratio of 1. We use Adam W optimizer (32) with constant learning rate of 1e-5, weight decay of 1e-4, and train for 10K steps. In Stage 2, we reuse the model from Stage 1, excluding the embedding layer since no external conditions are needed. One-third of the training iterations apply min- min-max optimization; the rest follow standard training using unrelated masks from WebVid M. Hyperparameters remain the same as in Stage 1. All experiments are conducted on 8 A800 GPUs (80GB each) and take about two days in total.
Training Details. For Stage 1, initialize model with Wan2.1-1.38
44
Inference Details. We perform inference using RTX 4090 GPUs. With an input resolution of 480p and frame length of 81, the inference takes approximately 24 seconds per video and consumes around 14GB peak GPU memory(DiT for 8GB, VAE decoding for 6GB), using 6 sampling steps.
Baselines. We compare method with ProPainter (56). VideoComposer
48 COCOCO [59). FloED [5)), DiffuEraser 25), VideoPainter (2 and VACE [2I]. We set the evaluate frame length as 32. To evaluate with same frame length, expand input frame length for VideoCompose [48] and FloED [5] The rest video inpainters are used their default frame length of their code bases. The frame resolutions used with their default resolutions.
Metrics. We evaluate background preservation using SSIM and PSNR. TC evaluates the temporal
consistency, follows COCOCO (59 and AVID (55) with CLIP- biT-h-ihh [4 to compute features. GPT-03 (36) serve as objective metrics. We evaluate these metrics on DAVIS datasets and 200 randomly selected Pexels videos to show generalizations across different datasets. Note that the
8

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200 Pexels videos are not contained in our training datasets, and masks are extracted by Grounded- SAM2. In the user study. participants are presented with multiple-choice questionnaire identify which video most effectively removed the target objects from the original video, without introducing blurring, visual artifacts, or hallucinated content within the masked areas.
Table 2:
Comparison of different methods on the DAVIS 40 and Pexels datasets. The best results are highlighted in bold. "TC" denotes temporal consistency, "VQ" stands for visual quality, and "Suce" represents the success rate.
Dataset
Pexels Dataset
Quantitative Results
GPT-03 Eval
User Preference
Quantitative Results
GPT-03 Eval
User Preference
Method
SSIM
PSNR
TC
VQ
Succ
SSIM
PSNR
TC
VQ
Suce
Propainter 56
0.9748
35.33
0.9769
5.68
56.67
34.55%
0.9746
35.76
0.9813
5.07
35.5
23.28%
VideoComp 43
0.8689
30.66
0.9529
2.12
7.78
0.25%
0.8865
30.26
0,9573
3.10
14.5
0.245%
COCOCO
0.8863
32.10
0.9511
3.40
12.22
1.96%
0.9145
30.84
0,9693
4.32
30.0
0.245%
FlEED
0,9350
0.9688
24.5
12.01%
DiffuEraser
25
0.9818
34.42
0.9767
5.71
56.67
42.40%
0.9859
34.41
0,9800
5.89
59.0
20.83%
VideoPuinter
0.9654
34.60
0.9620
3.80
17.98
1.47%
0,9821
36.49
0.9847
5.68
48.0
7.11%
VACE 21
0.9102
31.92
0.9747
3.12
8.89
2.21%%
0,9102
32.33
0.9898
6.27
49.5
23.77%
Ours (6 steps)
0.9842
36.56
0.9770
6.26
82.22
58.08%
0.9873
36.98
0,9905
6.87
76.5
62.01%
Ours (12 stops)
0.9846
36.62
0.9772
6.36
63.24%
0.9872
0.9906
80.5
67.15%
Ours (50 steps)
0.9847
36.66
0.9776
6.48
91.11
64.22%
0.9878
36.9%
0,9905
6.90
81.0
63.97%
5.1
Quantitative Comparison
As show in Table 2, our method outperforms previous baselines all 90 DAVIS videos, achieving an SSIM of 0.9847 and PSNR of 36.66. Notably, even with only sampling steps, our approach can generate high-quality videos while effectively preserving background details. Furthermore, our method exhibits superior temporal consistency, significantly outperforming generative models such VACE (21). and even surpassing the traditional inpainting method Propainter (56). These results demonstrate that our model consistently produces visually pleasing and high-quality video object removal. A similar trend is observed on 200 Pexels videos, where our method achieves the highest SSIM, PSNR, and temporal consistency scores. Moreover, reducing the number of sampling steps does not significantly degrade the removal performance.
artifacts
Artifaet
artifacts
Org Video
VideoComposer
COCOCO
FleED
Ours-6 Steps
Blur
Blur
Un umdestred
undestred object
Propainter
Diffueraser
VideoPainter
VACE
Ours-50 Steps
undesired object
artifacts
Org Video
VidpoCumposer
COCOCO
FleED
Ours-6 Steps
blur
artifacts
undessre object
objjct
Propainter
Diffueraser
VideaPainter
VACE
Ours-50 Steps
Figure 4: The visual results of our object remover. The video the left depicts the original video, while the video on the right displays the edited videos. Best viewed with Acrobat Reader. Click the images play the animation clips.

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5.2 Qualitative Results
To assess the visual quality and object removal success rate, we utilize GPT-03 13 a powerful reasoning large language model, by querying it with evaluation prompts. The quality score ranges from 1 (worst) to 10 (best). According GPT-O3 evaluation, our method achieves a higher score of 6.48, compared to 5.71 from the best previous method, indicating clearer and more visually appealing removal results. Regarding removal success rate, we prompt the GPT-O3 to determine whether the target objects were effectively removed. Our method achieves remarkable 91.1 91.11% success rate on the DAVIS dataset, far surpassing the previous best of 56.67% %. On the Pexels dataset, our method also outperforms previous state-of-the-arr approaches, with an 81 % success rate compared to 59.0%. Additionally, our method achieves higher score 6.90, versus 6.27 from prior best methods. For the user preference, it has similar trends, our method achieves the best score on both two datasets compared with previous best remover, 64% vs 42.40% and 67. 15% vs 23.77%, respectively.
Table 3: Ablation Study for two stages' training. The best results are boldfaced.
Quantitative Results
-GPT-03 Evaluation
Method
Stage
Structure
Condition
SSIM
PSNR
TC
Visual Quality
Success Rate
Ab-1
Stage
ShiftTable+CCoss
Prompt
0.9737
34.77
0.9756
6.27
51.11
Ab-2
Stage
Shifrhabls-Cros-Atm
Tokens
0.9747
35.09
0,9752
6.37
$6.67
Ab.3
Stage 1
ShifTTable
Tokens
0.9682
34.87
0.9743
6.42
53.33
Ab-4
Stage
ShiffTable+Setf-Artt
Tokens
0.9798
35.87
0.9773
6.39
71.11
-Quaniitative Results
-GPT-03 Evaluation
Method
Stage
Data Type
Input Noise
SSIM
PSNR
TC
Visual Quality
Success Rate
Ab-1
Stage 2
WebVid Data
Random Noise
0.9781
35.49
0.9759
6.27
65.56
Ab-2
Stage
Human Data
Random Noise
0.9796
35,21
0.9772
6.36
72.22
Ab-3
Stage 2
Human Data
Bad Noise Adv
0.9847
36.66
0.9776
6.48
91.11
5.3
Ablation Study
To understand the impact of each component and modification in our method, we conduct a step-by- step ablation study. All experiments use 50 sampling steps.
Stage 1. We begin by examining the role of the text encoder and prompt-based conditioning. In the comparison between Ab-1 and Ab-2 (Table B. we replace the text encoder and prompts with learnable contrastive tokens. The results show no significant drop in performance, indicating that the text encoder is redundant for the removal task when suitable learnable tokens are used instead. Next, comparing Ab-2 and Ab-3, we observe a slight performance degradation after removing the cross-attention module from the DIT. However, when introduce learnable contrastive condition tokens into the self-attention layers (Ab-4), the results not only recovers but also surpass that of Ab-1, This demonstrates the effectiveness of our simplified DIT architecture.
We compare models trained with and without vs. Ab-2) show that using manually labeled data alone does not significantly improve performance, likely due to the limited size (10K videos) and diversity of the dataset, which hinders generalization. Furthermore, compare different noise types used during training (Ab-2 to Ab-3). We find that adding "bad noise" (artificially degraded inputs) into training helps improve performance significantly.
Stage 2.
aanaaan-dntttted data. The results (Ab-1
Conclusion
In this paper, we propose MiniMax Remover, a two-stage framework for object removal in videos. In Stage 1, we simplify the pretrained DiT by removing cross-attention and replacing prompt embeddings with contrastive condition tokens. In Stage 2, we apply min-max optimization: the max step searches for challenging noise inputs that lead to failure cases, while the min step trains the model to successfully reconstruct the target from these adversarial inputs. Through this two-stage training, our method achieves cleaner and more visually pleasing removal results. Since it requires no classifier-fis free guidance (CFG) and uses only 6 sampling steps, inference is significantly faster. Extensive experiments demonstrate that our model delivers impressive removal performance across multiple benchmarks.
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for what
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More Details of Stage-1 Training
Here, we provide further details the training process of Stage 1. We adopt the mask selection strategy as in Senorita-Remova [58]. this paper, we propose to simplify the pretrained DiT architecture and replace the prompts with learnable contrastive condition tokens.
The most challenging issue object removal the undesired regeneration problem, where the model tends to regenerate objects in the masked region that share similar shape to the mask itself. To address this issue, as illustrated in Figure 3 we introduce contrastive condition tokens: a positive token ct guide the model towards object removal, and negative token e to guide object generation within the masked region. This allows the model to leverage CFG for effective object removal while preventing the reappearance of similar objects in the masked areas.
In Figure 5(a) a), when selecting masks, we randomly select masks from other unrelated videos, and paste the masks into the input video. These masks are designed to differ in shape from the actual objects within the masked region of the imput video. As result, the model, trained on such data, tends to generate content that differs from the mask shape during inference. This process is considered as positive condition e+. The position condition learns to remove masked objects.
In Figure b))), we incorporate training samples using their corresponding precise masks. In these cases, the masked region contains an object whose shape closely matches the mask. Training on such data encourages the model to regenerate similar objects in the masked region when conditioned on c This contrastive training strategy helps the model distinguish between removal generation tasks more effectively. This process is considered as the negative condition c The negative condition learns to generate the masked objects.
X N
Video
Masked Video
Patchify
DIT Block
Norm
Linear and Restaaee
Removal Pred
Prediction
MSE
Targe
Loss
Randomly Selected
Unselatd Mask
Simplified DiT
The maskad area doesn't contain shaped objects.
(8) Learn Removal
x N
Masked Video
Patchily
DIT Block
Norm
Lineir and Roslaape
Gen Prod
Prediction
MSE Loss
Target
SAM2
Predse Mask
The masked area contains same shaped objects.
Simplified DiT
(b) Learn Generation
Training framework of the Stage-1. 1. (a) denotes the positive condition process, and the position condition learns to remove the masked objects. and (b) represents the negative process, and the negative condition learns to generate the masked objects..
Figure 5:
8
Explanation Of Why CFG Can Be Discarded
8.1 MiniMax Optimization
In our work, we formulate the min- max remover using the following objective:
min max [uo(E", Zm, m) z 31
(13)
21

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where u is a DiT network parameterized by 0, and v" = c" - succe with Ssuce (a Here, Puxc video clip filtered and accepted by human annotators, and & the autoencoder. In practice, to search for the "bad" noise, we replace the inner maximization with:
min [uo("" 2m m) v²ll",
(14)
where v² = Zorg, and Zorg corresponds to an undesired generation. This approach finds a noise e" that causes us to fail. By subsequently training 0 minimize the objective even under such challenging noise, the model learns to produce outputs that deviate from the poor generation target v²
8.2 Minimax Optimization Encourages Correct Removal and Discourages "Bad" Removal
Minimax Optimization Encourages Correct Removal. Based on the principles of minimax optimization discussed above, we have that:
|| (uo(E, 2m, m) (E Z )1 & [//u0("" 2m, m. - (E* - = 1 1?2
(15)
This inequality implies that minimizing the loss on the "bad" input noise e* leads to improved performance on the clean input noise e. Consequently, the model is incentivized to generate outputs that increasingly align with desired removals.
Minimax Optimization Discourages "Bad" Removals. As shown in the formulation,
min (uu("" ;.m, m)) - (E - smcce) ||
the model is optimized to ensure that, upon convergence, no "bad" input noise e" can lead to a failure-inducing removal. Consequently, the model inherently avoids generating such "bad" removals.
In summary, this min-max training strategy enables the model avoid producing poor outputs (aligned with the Stage I negative condition c²) while encouraging alignment with high-quality generations (aligned with the Stage positive condition et). Therefore, after the minimax optimiza- tion, we can discard the classifier-free guidance (CFG), and the quality of removal results remains unaffected.
Experiments. Here, we conduct experiments to demonstrate that discarding CFG does not signif- icantly affect performance. To ensure fair comparison, we use the human-annotated data from Stage 2 and reuse the Stage model without removing the tokens injected into the self-attention layer. Thus, the ablation variant ab-l applies CFG by introducing two learnable contrastive tokens, c and T sampling process consists of 50 steps, while all other training and inference settings remain consistent with those described in the main text. As shown in Table 4 using CFG leads slight improvement in background preservation and temporal consistency, but results in marginal decreases in visual quality and success rate. These results further support that the model performs robustly even without CFG.
Table 4: Comparison of results with/without CFG on the DAVIS 40) dataset. TC indicates Temporal Consistency, while Suce Rate stands for Success Rate. The best results are boldfaced.
Quantitative Results
-GPT-03 Evaluation
Method
SSIM
PSNR
TC
Quality
Suce Rate
MiniMax-Remover(ww CFG)
0.9853
36.76
0.9778
6.45
90.00
CFG)
0.9847
36.66
0.9776
6.48
91.11
Why Use Adversarial-Based Noise?
9.1 Comparison of Adversarial-Baser Noise with Other Types of Noise
might seem intuitive to replace adversarial-based noise with either random or inversion- based noise. However, our experiments clearly justify the use of adversarial noise.
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Table 5: Ablation study different noise types using the DAVIS dataset 40
TC denotes Temporal
Consistency, and Suce Rate indicates Success Rate. The best results are highlighted in bold.
Quantitative Metrics
GGPT-03 Evaluation
Method
Input Noise
SSIM
PSNR
TC
Quality
Suce Rate
Ablation Study-1
Random Noise
0.9796
35.21
0.9772
6.36
72.22
Ablation Study-2
Inversion-Basee Noise
0.9797
35.80
0.9768
5.81
70.00
Ablation Study-3 (Ours)
Adversarial-Be Noise
0.9847
36.66
0.9776
6.48
91.11
Random noise fails to guide the model towards adversarial directions, As result, when training on limited data for large number of iterations, often leads overfitting without improving model robustness or performance.
Org Video
Random Noise
Removal Results
Noisc Histogram
QQ-Plot
sepsn2
200
seps=2
200
Steps=2
Stops=2
10
25
50
200
Figure 6: Visual results of the Stage-1 1 Object Remover under inversion- based noises. The top row presents the original video, random noise, removal results, the corresponding histogram, and QQ plot. Rows 2 to 5 the bottom display the removal results with inversion-based noise, the inversion-based noise itself, and its associated histogram and QQ plot, respectively. Best viewed with Acrobat Reader. Click the images play the animation clips.
unversion-based noise, on the other hand, typically requires many optimization steps to reconstruct noise from latent representations. To keep the computational cost comparable to adversarial noise, we restrict this process to just three steps. However, such shallow inversion retains significant amount of information from the latents, violating the assumption that noise follows standard Gaussian distribution (0, I). Consequently, it cannot be considered "random" noise in
contrast, adversarial noise is constructed by updating the input noise in a direction that maximizes model failure, while still keeping it within the // I) distribution. Specifically, we apply the update
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rule:
E" a'vI-aceva sign |e']
This allows us to craft noise that remains statistically valid but strategically challenging for the model.
As shown in Table 5 sedversarialbbasee noise significantly outperforms both random and inversion- based noise across all evaluation metrics, demonstrating its effectiveness in improving model robust- ness and generalization.
9.2 Determining Whether the Noise is Gaussian
To assess whether the noise follows a Gaussian distribution, we typically use visual tools such as histograms and QQ-plots. The histogram provides overview of the noise distribution, while the QQ-plot compares the quantiles of the observed noise with those of a standard Gaussian distribution. If the points in the QQ-plot lie approximately along straight line, this suggests that the noise is likely Gaussian.
Org Video
Randum Noise
Removal Resalts
Noise Histogram
QQ Plot
0=0.001
0.1
0.2
0.5
0.8
a=0000
0.1
0.2
0.5
0.8
0=0.001
a=0.001
0.1
0.2
Figure 7: Visual results of the Stage-1 1 Object Remover under adversarial-basee noises. The top row illustrates the original video, random noise input, the resulting object removal output, along with the corresponding noise histogram and QQ plot. The lower section comprises the object removal results under adversarial-bart and its associated histogram and QQ plot (fourth row). Best viewed with Acrobat Reader. Click the images play the animation clips.
noise (second row), visualization the adversarial noise (third row),
Inversion- -aasid Noise Non-Gaussian. As shown in Figure O both the histogram and QQ-plot reveal a clear deviation from the characteristics of Gaussian noise. Inversion based noise exhibits a distribution that significantly differs from that of random noise. These visualizations support the conclusion that inversion-based noise does not follow Gaussian distribution and is therefore unsuitable for training purposes. Moreover, the visualizations suggest that inversion- noise retains information from the original latent representations, further compromising its utility pure

---

noise. In addition, generating such noise requires multiple iterations of the inversion process, making computationally expensive and impractical for aurge-scale applications.
Eiversarial-Basee Noise Approximates a Gaussian Distribution. Figure 7 presents the histogram and QQ-plot for adversarial-based noise. The visual evidence indicates that this type of noise closely approximates Gaussian distribution. The visualizations also suggest that the noise is random and does retain any information from the original latent representations. Both the histogram and QQ-plot are highly similar to those of standard Gaussian random noise. As result, adversarial- noise not only effectively perturbs model predictions but also serves as suitable and reliable noise source for training. Notably, it can be generated efficiently using a single step of backpropagation.
Figure 8: Visual Results the Minimax- Remover with Adversarial- -Based Noise. Adversarial-based noise is integrated into the Minimax-Rent Remover yielding visually robust and consistent removal performance. Best viewed with Acrobat Reader. Click the images to play the animation clips.
Minimax-Remover
9.3 Testing Adversarial-Basse Noise the Robust ViniMMxxRemover
We assess the performance of the robust Minimax-Remover when subjected to adversarial based noise. Specifically, we apply adversarial noise to Minimax-Remove and gradually increase the noise level from 0.001 to 0.8. As illustrated in Figure 8 our method successfully removes objects from video frames effectively, maintaining high visual quality when < 0.5. Even as the noise level increases beyond 0.5, the removal results may become slightly blurred, but no regenerated objects or visual artifacts are observed. This demonstrates that the MiniMax-Remover resilient high levels of adversarial-ban -baee noise and can still produce clean and reliable removal results under challenging conditions.
10 The Prompt used for GPT-03 Evaluation
The evaluation is conducted using the 111-03-2025-0--11 model. We employ the following prompt assess visual quality and success rate.
Prompt for Quality Evaluation
Human: Please evaluate the visual quality the object-removal result this image pair: the left panel shows the original frame with the target object highlighted by a blue mask, and the right panel shows the frame after the object has been removed. Rate the quality of the removal
scale from to 10, where 10 means seamless, high-resolution result with no visible artifacts, 5 indicates some noise or artifacts but overall acceptable quality, and corresponds to unacceptable, very poor result. Return only the numeric score.
<Image> Image 1</Image> <lmage> Image 2</Image>
GPT-03:
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Prompt for Success Rate Evaluation
This is video object removal task. The object to be removed is already marked in blue in the image on the left. Please determine whether the object has been successfully removed. successful removal is defined by the following criteria:
Human:
1. The object has been completely removed.
There is no visible blurriness in the removal area (not background blur, but unnatural foreground blur that is inconsistent with the surrounding context).
3. There are no moiré patterns artifacts in the region (e.g.. unnatural textures that differ significantly from human visual expectations).
No new, unwanted objects have been generated in the area (e.g., the tiger is removed but a bear appears instead, or another tiger is generated).
Shadows not in the masked region are considered successfully removed. Think it step by step. Then provide final judgment by answering with "yes" or "no"
<Image> Image 1</Image>
<Image> Image 2</Image>
GPT-03:
11 More Experimental Results
11.1
Evaluation of ReMOVE Metries
Table Comparison on the ReMOVE benchmark (higher is better).
Method
ReMOVE (w/o Crop)
ReMOVE (w/ Crop)
ProPainter 56
0.8634
0.9259
FloED 15
0.6950
0.8103
DiffuEraser
25
0.8621
0.9212
COCOCO [59
0.8009
0,8994
VideoComposer 48
0.6605
0,7429
VideoPainter 2
0.6955
0.8279
VACE [2]
0.5731
0.7723
Mimovax-Remover
Remover (6 steps)
0.8831
0.9401
MiniMax- (12 steps)
0.8843
0,9405
Remover (50 steps)
0.8850
0.9416
Table reeports quantitative comparisons on the ReMOVE benchmark 77 a reference-free metric for evaluating object removal quality (higher is better). Our method consistently achieves the best performance under both ReMOVE (w/o Crop) and ReMOVE (w/ Crop) settings. Specifically, our 50-step model attains scores of 0.8850 and 0.9416, outperforming the strongest baseline ProPainter by +0.0216 and +0.0157, respectively. These results demonstrate the superior removal fidelity and spatial consistency of our approach.
Across methods, the Crop results are higher than the w/o Crop results, indicating that localized evaluation around the target region is generally easier. Nevertheless, our model maintains the highest accuracy both configurations, highlighting its robustness to complex backgrounds and global contextual variations.
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Overall, our model demonstrates strong generalization and stability across different evaluation conditions, surpassing prior state-of-the the-trrertt methods such as ProPainter [56], DiffuEraser [25], and COCOCO 55 on both global and localized ReMOVE metrics.
11.2
Can GPT-O3 be trusted?
Table 7: Consistency with human annotations on 100 removal samples.
Assessment Type
GPT-40
GPT-4O-mini
GPT-4-turbo
GPT-03
Acc in Failure Cases
94.0%
98.0%
86.0%
96.0% %
Acc in Success Cases
88.0%
26.0%
58.0%
94.0 %
Overall Accuracy
91.0%
62.0%
72.0%
95.0% %
Table 8: Consistency with human annotations on 100 multi-object videos.
Assessment Type
GPT-40
GPT-4O-mini
GPT-4-turbo
GPT-03
Acc in Failure Cases
90.0%
92.0%
86.0%
98.0%
Acc in Success Cases
90.0%
48.0%
48.0%
86.0%
Overall Accuracy
90.0%
70.0%
67.0%
92.0%
We evaluate four models masks to highlight the removed regions for evaluation. As reported in Table 7 GPT-03 attains 96.0% accuracy in fallure cases and 94.0% in success cases for 95.0% overall, GPT-4O records 94.0% and 88.0% for 91.0% overall, GPT-4-turbo reaches 86.0% and 58.0% for 72.0% overall, and GPT-4O-mini achieves 98.0% and 26.0% for 62.0% overall. Table summarizes the 100 multi object video removals where GPT-03 again leads with 98.0% in failure cases and 86.0% in success cases for 92.0% overall, while GPT-40 achieves 90.0% in both overall and success accuracy and GPT-4-turbo and GPT-4O -ini reach 67. 67.0% and 70.0% overall respectively. Together Tables 7 and & show that GPT-03 aligns best with human annotations by balancing recognition of failures and true successes.
datasets with balanced splits of successes and failures and use
11.3 Results on Multi-Object Videos
We evaluate on multi-mask subset of DAVIS to stress multi-object editing and report SSIM, PSNR, Temporal Consistency, Visual Quality, and Success Rate in Table 9 MiniMax-Remove achieves the best overall results, with the 50-step variant reaching SSIM 0.9847, PSNR 36.69, TC 0.9780. VQ 6.70, and Suce 92.59%. This clearly outperforms strong baselines such as ProPainter at SSIM 0.9778 and PSNR 35.64 and DiffuEraser at SSIM 0.9769 and PSNR 35.08, while also yiclding much higher VQ and task success. Traditional inpainting methods show reasonable pixel metrics but struggle in complex multi-mask interactions, which leads to lower VQ and Succ,
with VideoComposer at VQ 2.13 and Suce 9.26%. Performance scales with sampling steps for our method: 6 to 12 steps provides notable gain, and 12 50 steps offers smaller but consistent improvements that help on hard cases. Overall, MiiniMax-Removee balances temporal stability and detail restoration most effectively on DAVIS multi-mask videos.
11.4 Results on Dynamic Background Videos
As shown in Table 10 our method consistently achieves the best results among all methods on the DAVIS dynamic background subset. The 50-step version reaches the highest SSIM of 0.9828, PSNR of 36.48, and success rate of 91.36, showing clear superiority in both reconstruction and perceptual quality. Traditional methods such as ProPainter and DiffuEraser fall behind success rate and VQ. Increasing steps improves MiniMax-Remover steadily, confirming the robustness of iterative refinement. Overall, Remover maintains strong temporal consistency while significantly enhancing visual fidelity and task success compared with prior basclines.
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Table Comparison of methods on multi-objects videos. TC denotes Temporal Consistency. VQ denotes Visual Quality, and Succ denotes Success Rate.
Method
SSIM
PSNR
TC
VQ
Succ
ProPainter
56
0.9778
35.64
0.9773
5.67
57.41
VideoComposer
48
0.8967
31.14
0.9546
2.13
9.26
COCOCO 59
0,9101
32.98
0.9523
3.37
11.11
FLoED
15
0.8887
32.72
0.9691
5.09
42.59
DiffuEraser
0.9769
35.08
0.9790
5.81
57.41
VideoPainter
0.9652
35.17
0.9634
5.00
24.44
VACE 21
0.8949
32.67
0.9732
3.20
5.56
MiniMax- Remover (6 steps)
0.9842
36.62
0.9773
6.41
85.18
MiniMax Remover (12 steps)
MiniMax-Remover
0.9843
36.64
0.9778
6.31
87.04
MiniMax- Remover (50 steps)
0.9847
36.69
0.9780
6.70
92.59
Table 10: Comparison of different methods dynamic background videos. TC denotes Temporal Consistency, VQ denotes Visual Quality, and Succ denotes Success Rate,
Method
SSIM
PSNR
TC
VQ
Suce
ProPainter [56
0.9781
35.78
0.9778
5.53
53.09
COCOCO
59
0.9125
33.26
0.9546
3.29
12.35
Fl.oED
0.8892
32.75
0.9663
5.07
43.21
DiffuEraser
25
0.9741
35.12
0.9778
5.68
55.56
VideoComposer 48
0.8936
31.31
0.9531
2.13
8.64
VidenPainter 2
0.9652
35.49
0.9623
3.88
19.75
VACE 21
0.8942
32,72
0.9762
2.97
7.41
MiniMax Remover (6 steps)
0.9821
36.42
0.9786
6.16
81.48
MiniMex-Remover (12 steps)
0.9823
36.46
0.9787
86.42
MiniMax- (50 steps)
0.9828
36.48
0.9789
6.46
91.36
Table 11: Effect of dilation size on denotes Visual Quality, and Suee denotes Success Rate.
TC denotes Temporal Consistency, VQ
Dilation
SSIM
PSNR
TC
VQ
Suce
0.9830
36.49
0.9764
6.37
91.11
0.9847
36.66
0.9776
6.48
91.11
12
0.9838
36.38
0.9778
6,40
92.22
24
0.9829
36.36
0.9789
6.33
84.44
11.5 Tightness/Rooseness of the mask
As shown in the Table moderate dilation values yield the best balance between structure and temporal smoothness. Dilation achieves the highest SSIM of 0.9847 and PSNR 66.66 with strong visual quality, while dilation 12 gives the best success rate of 92.22. Very small or large dilation slightly reduce performance, indicating that moderate spatial context improves reconstruction and temporal consistency.
11.6 The Implementation of Learnable Contrastive Tokens
28

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import torch
import torch
class TokenEmbedding (nn. Module):
def -_init__ (self, dim):
-- -nnt- self. dim dim The first token represents the # The second token represents the C-. un an. Eww..ddddigggg (2, dim)
10
self
using 6 tokens to enhance contrastive tokens. self iif.mi mlp nn Linear (dim, dim) self.act_fa nn unLUi
12
13
14
def forward input_ids).
baz input_ids input_ids.size
17
x
(self. token_ goken_embedding (input_ids) x self view (baz, -1, 6, colf.dim) return
18
19
21
class Stage1_ (nn. Module):
22
def -ninit_ (sllf, config):
23
innit))
self token_ embed TokenEmbedding (dim)
26
27
28
def (peaf sidden_staten input_ids):
30
tokens self token_ saabed(ing
embeutintttt_ide (input_ids)
31
hidden_states shape [1]
32
hidden_ staden_states torch.cal ([hidee_statees tokens], dim=1)
hidden_stater self attn
key, value) [:,
35
36
return hidden_states
Pseudo Codes for Stage-1 Model.
11.7
The Impact of Learnable Token Number
Table 12: Comparison of different token configurations in stage- TC denotes Temporal Consistency, VQ denotes Visual Quality, and Suce denotes Success Rate.
Method
Tokens
SSIM
PSNR
TC
VQ
Suce
stage-1
0.9778
35.82
0.9774
6.13
66.67
stage-1
0.9801
35.88
0.9776
6.23
70.00
stage-1
0.9798
35.87
0.9773
6.39
71.11
stage-1
0.9792
35.85
0.9772
6.46
71.11
As shown in Table 12 using six tokens achieves the best trade-off across metrics. While the two-token setting slightly improves SSIM and PSNR, the six-token model attains the highest VQ score (6.39) and the best success rate (71.11 %), indicating superior overall quality and stability. Increasing to eight tokens yields no further gain and even reduces SSIM and PSNR, suggesting fremefragmenttaion. Therefore, the six-token configuration provides the optimal balance between reconstruction fidelity and perceptual consistency.
29

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12 Broader Impacts
Hemimax-Remover is diffusion- model for removing objects from videos only 6 inference steps without CFG. It enables efficient video editing, film post-production by simplifying complex visual manipulation. However, like all generative models, it could be misused to create deceptive or fake videos, potentially causing misinformation or reputational harm. To mitigate such risks, we will release the model under CC BY-NC 4.0 license, restricting commercial use and large-scale deployment.
13 Limitation and Future Work
In this study, we present a fast and effective method for video object removal that does not rely on CFG. However, our approach has some limitations. First, the object remover in Stage was trained using only 10,000 videos. Second, while the DiT model is efficient, large portion of the computational overhead comes from the VAE encoding and decoding processes. In future work, plan to expand the dataset to enhance the remover's 's robustness and explore the use of smaller VAE speed up inference.
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Reviews:
[{"review_id": "7VE5ygbSI5", "reviewer": "Reviewer_KYyp", "summary": "**MiniMax-Remover**\n\nThe authors propose a model for video object removal which addresses a major limitation of existing methods: slow inference time. They claimthis is mainly due to expensive sampling and the use of classifier free guidance. \n\nThe method includes two stages, where the authors train an object removal model in stage 1 by removing the CA layers and introducing contrastive condition tokens to replace textual guidance. Since this still uses CFG with 50 timesteps and generates artifacts, the authors enhance the model in stage 2 improve editing quality and inference speed.\n\nOverall, the paper is well written and a good read.", "questions": "- can this method do multi object removal? what if the binary mask covers multiple objects?\n- will the code and the trained model be made public?", "limitations": "please refer to weaknesses", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "*Strengths*\n- No text prompts required to the model.\n- Lightweight as the cross-attention layers are removed, making it efficient\n- Requires very few sampling steps (~6) to generate high quality results compared to ~50 in general.\n\n*Weaknesses*\n- Although the model doesn't depend on thre text prompt, the mask in L231 is generated using text prompts, so the method indirectly depends on the text prompt.\n- Why have no video metrics been used except the temporal consistency metric? Additionally, bakcground removal metrics such as ReMOVE (CVPRW24; A Reference-free Metric for Object Erasure) can be used to evaluate inpainting efficacy per-frame.\n- Can the \"success-rate\" from a model like GPT-03 be trusted? especially in cases when there are multiple similar objects in the scene? \n- Looks like all the methods have very high TC and even a model with very low success rate also has a very high temporal consistency, which is weird. \n- From the video in Figure 1, we can observe that the model does in remove shadows (branches on the top-right video), reflections (Figure 4) or add in additional information (lighting in the bottom left video). Some of the added content also seems slightly blurry, and could impact quality at higher resolutions.\n- no mention about multi-object removal.\n- the failures of this method have not been discussed. are there no cases where random objects have been added post stage 2?\n- There is no ablation done on the tightness/looseness of the mask (as SAM generates very tight masks) to simulate real-world scenarios.", "was_revised": true, "final_justification": "The additional experiments have answered my questions. Thus, I'm increasing my score. Hope the authors add these additional results to the updated paper.", "rebuttal": "We sincerely thank you for your time and effort in reviewing our paper. Your comments are valuable in helping us improve the quality of our work.\n\n* *The Minimax-Remover adopts a two-stage training strategy. In Stage 1, we train a basic remover using contrastive conditions. In Stage 2, we apply minimax optimization to effectively tame the bad noise. This approach leads to a more stable and faster video object remover.*\n\n* *We have open-sourced both the code and model weights, which are now available on mainstream opensource platforms. Since its release, the Minimax-Remover has received considerable attention from the community. Notably, users have developed and integrated it into a well-known ComfyUI node.*\n\n---\n\n***Kindly note that, in accordance with NeurIPS guidelines, we are not permitted to submit external links or updated pdfs of the paper during the rebuttal and discussion stages. Therefore, we are only able to provide experimental results at this time.***\n\n---\n\n**Q1: The method indirectly depends on the text prompt.**\n\nR1: We would like to clarify that the text prompt is only used during the data preparation phase to construct the training dataset with video-mask pairs. The prompt is not involved in either the training process or the inference stage. \n\n\n&nbsp;\n\n**Q2: Why have no video metrics been used except the temporal consistency metric? Bakcground removal metrics (ReMOVE, CVPRW24) can be used to evaluate inpainting efficacy per-frame.**\n\nR2: Thank you for your valuable suggestion! \n\n* Besides the temporal consistency, we use the SSIM and PSNR to measure the background preservation, and GPT-O3 to evaluate the frames in video. \n\n* We appreciate your insightful suggestion to include ReMOVE evaluation metric, which we had not previously incorporated. After receiving your feedback, we evaluated our Minimax-Remover using this metric. The results are shown below:\n\n| Method           | ReMOVE (w/o Crop) | ReMOVE (w/ Crop) |\n|------|------|------|\n| Vace             | 0.5731            | 0.7723           |\n| ProPainter       | 0.8634            | 0.9259        |\n| FloED            | 0.6950            | 0.8103          |\n| DiffuEraser      | 0.8621            | 0.9212        |\n| CoCoCo           | 0.8009            | 0.8994       |\n| VideoComposer    | 0.6605            | 0.7429  |\n| VideoPainter     | 0.6955            | 0.8279      |\n| ours-6 steps     | 0.8831            | 0.9401       |\n| ours-12 steps    | 0.8843            | 0.9405      |\n| **ours-50 steps**| **0.8850**        | **0.9416**  |\n\nAs shown in the table, we can find that our method outperforms all baselines according to the ReMOVE metric.\n\n&nbsp;\n\n**Q3： Can the \"success-rate\" from a model like GPT-O3 be trusted? especially in cases when there are multiple similar objects in the scene?**\n\nR3: Thanks for pointing out this problem.\n\n* We used different LLMs to evaluate the removal results, including GPT-4O, GPT-4O-mini, GPT-4-turbo and GPT-O3, on 100 removal samples with 50 success samples and 50 failure samples annotated by human. We use the mask to highlight the removed region to let the LLM know where to focus.  **Notably, we find GPT-O3 has the highest consistency with human annotation.** The results are shown below:\n\n| Success Rate         | GPT-4O| GPT-4O-mini| GPT-4-turbo | GPT-O3    |\n|----------------------|-------|------------|-------------|-----------|\n| Acc in Failure Cases | 94.0% | 98.0%      | 86.0%       | **96.0%** |\n| Acc in Success Cases | 88.0% | 26.0%      | 58.0%       | **94.0%** |\n| Overall Accuracy     | 91.0% | 62.0%      | 72.0%       | **95.0%** |\n\n* We also choose 100 multiple objects that removed from video to test the LLM, with 50 success cases and 50 failure cases.\n\n| Success Rate   | GPT-4O    | GPT-4O-mini | GPT-4-turbo | GPT-O3    |\n|----------------------|-----------|-------------|-------------|-----------|\n| Acc in Failure Cases | 90.0%     | 92.0%       | 86.0%       | **98.0%** |\n| Acc in Success Cases | **90.0%** | 48.0%       | 48.0%       | 86.0%     |\n| Overall Accuracy     | 90.0%     | 70.0%       | 67.0%       | **92.0%** |\n\nThe table demonstrates that GPT-O3 demonstrates high consistency with human annotations, rather than other LLMs.\n\n\n&nbsp;\n\n**Q4: High Temporal-Consistency (TC) but very low success rate.**\n\nR4:  We want to clarify that the two metrics are not contradictory. \n\n*\tThe first metric evaluate the motion smoothness of the video. \n\n*\tThe second metric evaluation is an overall metric, which evaluates motion smoothness, artifacts, blur and undisired objects. A video with blur or undesired objects can also achieve high Temporal-Consistency.\n\n*For example, a failure removal case that add undesired object can have good temporal consistency.* LLM will mark this video as failure case but it achieves a high TC score.\n\n\n&nbsp;\n\n**Q5: The model does not remove shadows. The video has slight blur.**\n\nR5: Thank you for your comments. \n\nWe would like to clarify that:\n\n* The shadow issue arises because we do not provide a shadow mask or reflection mask to our model. This problem can be solved by providing both object and shadow/reflection masks to the removal model. \n\n* Upon careful inspection of the original frames, we observed that the lighting artifact is present even before the removal process and outside the masked region. This suggests that it is not an artifact introduced by removal, but rather a persistent feature of the background.\n \n* As for the slight blur, we observed that it does not impact the visual quality at normal resolution. Performing inference at a higher resolution can produce higher-quality results.\n\n\n\n&nbsp;\n\n\n**Q6: Multi-Object Removal.**\n\nWe would like to point out that our method performs effectively on the **DAVIS** dataset, where approximately **60%** of the videos contain multi-object masks. Additionally, the demo video provided in the supplementary materials includes several examples of multi-object removal. We provide the evaluation on multi-objects videos as shown in the following.\n\n*Some Multi-Object Removal Examples of Demo Video in Supplementary File*\n\n- **1:40 left** – a stick and a flower pot  \n- **1:40 right** – two people  \n- **1:50 right** – the masks related to the person are separated  \n- **2:14 bottom** – multiple people boxing  \n- **2:34 top** – multiple goldfish  \n- **2:34 bottom** – person and backpack  \n- **2:40 top** – koala’s hands and body are separate masks  \n- **2:40 bottom** – surfboard, person, and rope/line  \n- **2:46 bottom** – a crowd of people  \n- **2:52 top** – person and motorcycle  \n- **2:52 bottom** – person and motorcycle  \n- **2:56 top** – person and skateboard  \n\n*Multi-Object Evaluation Results*\n\n| Method                     | SSIM   | PSNR  | TC     | VQ   | Succ   |\n|----------------------------|--------|-------|--------|------|--------|\n| ProPainter                 | 0.9778 | 35.64 | 0.9773 | 5.67 | 57.41  |\n| VideoComposer              | 0.8967 | 31.14 | 0.9546 | 2.13 | 9.26   |\n| CoCoCo                     | 0.9101 | 32.98 | 0.9523 | 3.37 | 11.11  |\n| FLoED                      | 0.8887 | 32.72 | 0.9691 | 5.09 | 42.59  |\n| DiffuEraser                | 0.9769 | 35.08 | 0.9790 | 5.81 | 57.41  |\n| VideoPainter               | 0.9652 | 35.17 | 0.9634 | 5.00 | 24.44  |\n| VACE                       | 0.8949 | 32.67 | 0.9732 | 3.20 | 5.56   |\n| MiniMax-Remover (6 steps)  | 0.9842 | 36.62 | 0.9773 | 6.41 | 85.18  |\n| MiniMax-Remover (12 steps) | 0.9843 | 36.64 | 0.9778 | 6.31 | 87.04  |\n| MiniMax-Remover (50 steps) | **0.9847** | **36.69** | **0.9780** | **6.70** | **92.59** |\n\nAs shown in the table, MiniMax-Remover consistently outperforms all baselines across all metrics in the multi-object removal setting.\n\n\n&nbsp;\n\n**Q7: The failures of this method have not been discussed. Are there no cases where random objects have been added post stage 2?**\n\nR7: Thanks for your advice. We will put the failure cases in our paper in the revised version. Generally speaking, our minimax-remover is robust in most scenerios. However, we observe it has higher failure rate in extreme long video, such as videos with over than 500 frames. Moreover, it has some failure rate in extreme loose masks (e.g., looseness=32 dilation).\n\n&nbsp;\n\n**Q8: Ablation done on the tightness/looseness of the mask.**\n\nThanks for your advice, we have supplied the tightness/looseness of the mask in the following table. The higher dilation hyperparameter means looser masks.\n\n| Method| Dilation|SSIM |PSNR |TC| VQ| Succ|\n|--------|-----|-----|-----|-----|-----|-----|\n| MiniMax-Remover| 2   |0.9830|36.49|0.9764|6.37|91.11|\n| MiniMax-Remover| 6   |**0.9847**|**36.66**|0.9776|**6.48**|91.11|\n| MiniMax-Remover| 12  |0.9838|36.38|0.9778|6.40|**92.22**|\n| MiniMax-Remover| 24  |0.9829|36.36|**0.9789**|6.33|84.44|\n\nAs shown in the table, our minimax-remover performs well in the reasonable masks. However, when giving it extreme loose masks, it will have higher failure rate.\n\n\n\n&nbsp;\n\n**Q9: Can this method do multi object removal? what if the binary mask covers multiple objects?**\n\nR9: Definately, our method can do multi-object removal. You can find the multiple object removal in our demo video from the supplementary file. Moreover, we also evaluate on the videos with multi-objects in DAVIS dataset. **Please refer to our response to Q6 for our experiments and explanation.**\n\n\n&nbsp;\n\n**Q10： Will the code and the trained model be made public?**\n\nR10：Sure, the code and trained model have been opensourced on github and huggingface. We provide the gradio demo with SAM2, users can click single object or multiple objects and then remove it from the video. Our minimax-remover receives a lot of attention from the public. It has been integrated in the ComfyUI node and is popular in the commuity.\n\n---\n\n**We sincerely hope you can reconsider your ratings based on our response. Your comments can highly help us to improve our submitted paper.**"}, {"review_id": "5QNxsGkSYy", "reviewer": "Reviewer_fNcQ", "summary": "This paper proposes \"MiniMax-Remover,\" a novel method designed to enhance the performance and efficiency of video object removal. The authors begin by highlighting three key problems associated with existing video object removal methods:\n\n- Generation of undesired content or artifacts, including occlusions and blurs within masked regions.\n- Heavy reliance on auxiliary priors, such as optical flow, text prompts, or DDIM inversion.\n- Requirement of numerous sampling steps for achieving high visual fidelity, coupled with dependence on CFG, thus demanding significant computational resources.\n\nTo overcome these limitations, the authors propose \"MiniMax-Remover,\" utilizing the pretrained Wan2.1-1.3B model as the foundation. They eliminate textual conditioning and all cross-attention layers from the pretrained DiT Block, effectively removing the dependency on auxiliary priors. The MiniMax-Remover method is structured around a two-stage training framework:\n\n- Stage 1 employs contrastive conditioning tokens (c+, c-) injected into self-attention layers to explicitly teach the model the distinction between object removal and object generation, enhancing model clarity and effectiveness.\n\n- Stage 2 involves further refining the model through a min-max optimization strategy:\n  - Stage 2-1 focuses on identifying and selecting challenging or \"bad noise\" scenarios that lead to model errors.\n  - Stage 2-2 utilizes a dataset of 17K videos from stage 1 results, consisting of 10K successfully removed object videos and 7K original videos, to perform adversarial training. The model is thus trained robustly to handle challenging inputs.\n\nOverall, this approach significantly enhances video object removal performance, delivering high-quality results with improved computational efficiency.", "questions": "1. In Section 3.3, \"we observe that these failure cases are strongly correlated with specific input noise patterns”, could you provide experimental evidence or additional analysis supporting this observation? Clarifying this point could significantly improve the understanding and validation of your proposed method.\n2. The explanation regarding the learnable contrastive tokens is somewhat unclear. Could you elaborate on the token embedding layer used and clarify how exactly these tokens are learned during training?\n3. In the supplemental video materials, objects appear to be effectively removed, but shadows remain visible. Can the method be extended or improved to naturally remove shadows along with the objects?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n- The paper is well-structured and clearly presented, making it accessible to readers.\n- Effectively leverages a lightweight DiT-based architecture, optimizing for both computational efficiency and practical performance.\n- Particularly noteworthy is the method’s ability to perform efficiently without relying on CFG, using only 6 sampling steps. As illustrated in Table 1, the proposed model has the smallest number of parameters, fastest inference, and lowest GPU memory usage, making it highly practical.\n- Provides a comprehensive ablation study clearly delineating individual contributions of each training stage (Stage 1 and Stage 2), offering valuable insights into the importance and effectiveness of each component.\n\n### Weaknesses\n\n- In Section 3.3, the claim \"we observe that these failure cases are strongly correlated with specific input noise patterns\" lacks robust experimental validation, making it challenging to conclusively support this statement.\n- Limitation of training data: The Stage 2 training relies heavily on manually selected data, presenting significant limitations:\n    - Manually selecting 10,000 successful object removal videos requires substantial human resources, making it expensive and difficult to scale.\n    - The definition of \"high-quality\" is inherently subjective, potentially introducing biases based on individual annotators’ preferences or criteria, thereby influencing the model’s overall performance.", "was_revised": true, "final_justification": "I think this paper not only addresses existing issues in video object removal but also proposes the most lightweight model to date. In the rebuttal, I inquired about the experimental analysis of bad noise as well as the limitations of the training data, and I received responses to both. Since my concerns have been addressed, I have decided to change my score from the original rating to Accept.", "rebuttal": "Thanks for your recognition of our work. As you mentioned, our paper *significantly enhances video object removal performance, delivering high-quality results with improved computational efficiency*. Your valuable comments help us to further improve our paper. The following are our responses to your questions.\n\n---\n\n***Please note that, in accordance with this year's NeurIPS guidelines, external links and updated PDFs are not permitted during the rebuttal and discussion stages. Consequently, we are unable to share an updated pdf or visualization results.***\n\n---\n\n**Q1: \"We observe that these failure cases are strongly correlated with specific input noise patterns\" lacks robust experimental validation, making it challenging to conclusively support this statement.**\n\nR1: Thanks for your comments.\n\n* When evaluating the model trained in Stage-1, we observed that simply changing the random seed often transformed failure cases into successful ones, or verse vice. This phenomenon occurred frequently. Based on this observation, we found that * the success or failure of the removal results is highly correlated with input noise*. \n\n* This observation inspired us to further investigate the characteristics of **\"bad\" noise**. To address its adverse effects, we proposed a **minimax optimization strategy** to mitigate the impact of such noise. The results obtained in Stage-2 significantly outperform those in Stage-1, which further supports our claim.\n\n* After receiving your comments, we conducted additional experiments to verify our claim. Specifically, we evaluated the Stage-1 model using three different random seeds (i.e., 0, 1, and 2). \n\nWe found that only 65.56% has consistent successful results (all three seeds resulted in successful outcomes).\n\n\n| Method| Succ|\n|--------|-----|\n| stage-1-seed-0       |71.11|\n| stage-1 seed-1       |72.22|\n| stage-1 seed-2       |75.56|\n|stage-1 success on all three seeds|65.56|\n\nAs shown in the above table, the stage-1 model is sensitive to random seed.\n\n&nbsp;\n\n**Q2: Manually selecting 10,000 successful object removal videos requires substantial human resources, making it expensive and difficult to scale**\n\nR2:  Thank you for your insightful comment. We would like to clarify that this method can scale up to large-scale datasets by the following two steps. First, we can apply our Stage-2 model to a large amount of videos and leverage GPT-O3 to identify and exclude failure cases. Second, we train a new remover model on the filtered data.\n\n&nbsp;\n\n**Q3: The definition of \"high-quality\" is inherently subjective, potentially introducing biases based on individual annotators’ preferences or criteria, thereby influencing the model’s overall performance.**\n\nR3: We asked three human annotators to filter the videos multiple times using a voting mechanism. Subsequently, each annotator reviewed the videos again to ensure that there were no artifacts, blurriness, or undesired objects in the masked regions. These two steps collectively ensure the quality of the data.\n\n&nbsp;\n\n**Q4: In Section 3.3, \"we observe that these failure cases are strongly correlated with specific input noise patterns”, could you provide experimental evidence or additional analysis supporting this observation? Clarifying this point could significantly improve the understanding and validation of your proposed method.**\n\nR4: Thank you for your valuable suggestion. We conducted experiments showing that the random seed significantly affects the success of the stage-1 model. We will include these results in the revised paper. Using three random seeds for object removal, we found that only 65.56% has consistent successful results (all three seeds resulted in successful outcomes).\n\n\n| Method| Succ|\n|--------|-----|\n| stage-1-seed-0       |71.11|\n| stage-1 seed-1       |72.22|\n| stage-1 seed-2       |75.56|\n|stage-1 success on all three seeds|65.56|\n\n| Method| SSIM |PSNR |TC| VQ| Succ|\n|--------|-----|-----|-----|-----|-----|\n| stage-1|0.9798|35.87|0.9773|6.39 |71.11|\n| stage-2|**0.9847**|**36.66** |**0.9776**|**6.48** |**91.11**|\n\nAs shown in the table, the stage-1 model is sensitive to random seed, after fine-tuning with the minimax optimization that aims to tame the \"bad noise\", the performance has significant increase. These two findings support our statement: *\"these failure cases are strongly correlated with specific input noise patterns.\"*\n\n\n&nbsp;\n\n**Q5：The explanation regarding the learnable contrastive tokens is somewhat unclear. Could you elaborate on the token embedding layer used and clarify how exactly these tokens are learned during training?**\n\nR5：Thanks for your reminder. Here, we provide the detailed analysis to our learnable contrastive tokens. \n\n* We implement the `TokenEmbedding` class using `nn.Embedding`, a `SiLU` activation function, and a `Linear` layer. This class is then instantiated within the `Stage1_Model` class. During training, the `hidden_states` are concatenated with contrastive tokens and processed through a self-attention mechanism. Backpropagation is applied to compute gradients for each layer, and the AdamW optimizer is used to update the weights, with a learning rate of `1e-5`.\n\n\n* The simplified code is shown below:\n\n```python\nimport torch\nimport torch.nn as nn\n\nclass TokenEmbedding(nn.Module):\n    def __init__(self, dim):\n        super().__init__()\n        self.dim = dim\n        # The first dimension represents the c^+\n        # The second dimension represents the c^-.\n        self.token_embedding = nn.Embedding(2, dim) \n        # using more tokens to expand the influence of the contrastive tokens.\n        self.mlp = nn.Linear(dim, 6 * dim) \n        self.act_fn = nn.SiLU()\n\n    def forward(self, input_ids):\n        bsz = input_ids.size(0)\n        x = self.act_fn(self.token_embedding(input_ids))\n        x = self.mlp(x).view(bsz, -1, 6, self.dim)\n        return x\n```\n\n```python\nclass Stage1_Model(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        ...\n        self.token_embed = TokenEmbedding(dim)\n        ...\n\n    def forward(self, hidden_states, input_ids):\n        ...\n        tokens = self.token_embed(input_ids)\n        l = hidden_states.shape[1]\n        hidden_states = torch.cat([hidden_states, tokens], dim=1)\n        ...\n        hidden_states = self.attn(query, key, value)[:,:l]\n        ...\n        return hidden_states\n```\n\n&nbsp;\n\n**Q6: In the supplemental video materials, objects appear to be effectively removed, but shadows remain visible. Can the method be extended or improved to naturally remove shadows along with the objects?**\n\nR6: The answer is yes.  \n\n* Our model can remove the shadow if the mask of shadow is given. We want to clarify that our model has the multi-objects removal ability. Many contributors in the community have already made many cases on the Internet.\n  \n* Honestly speaking, current model can remove shadow only if the mask of shadow is given. *We believe this is a data-intensive problem, if well-made video pairs are provided, our minimax-remover can be extended to naturally remove shadows along with the objects.*\n\n\n---\n\nWe sincerely appreciate your thoughtful feedback and the time you dedicated to reviewing our work. We hope that our responses and the clarifications have addressed your concerns effectively. \n\n**We kindly ask you to reconsider your score based on our extensive experiments  and clarification. Your reconsideration means a lot to us.**"}, {"review_id": "INLAD9MJ4R", "reviewer": "Reviewer_oy1i", "summary": "This paper proposes MiniMax-Remover, a two-stage approach for video object removal. In stage 1, the cross-attention layers are removed from the pretrained video generation model Wan2.1-1.3B. Instead, the learnable contrastive condition tokens are added to the module. Stage 2 employs a minimax optimization strategy as the inner maximization searches for adversarial bad noise, while the outer minimization trains the model to be robust against such noise. Experimental results show that the proposed method achieves state-of-the-art results with only 6 sampling steps.", "questions": "- How does the method perform on videos with dynamic backgrounds or multiple overlapping objects? \n- In the ablation study, the impact of positive or negative tokens should be further explored, such as removing one and evaluating performance degradation.  \n- In line 139, why split the feature into 6 tokens to increase control ability? How to choose the number of tokens?\n- In line 185, how to select 10K video pairs manually from the 17K generated by the Stage 1 model?\n- In Table 3, the GPT-O3 is used for evaluation. How to verify that large models could replace manual scoring?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:  \n- The paper is well organized and well written.\n- The idea is interesting and effective. By eliminating CFG and reducing sampling steps, the method significantly speeds up inference while maintaining the quality.\n- Experimental results show the superiority of the proposed method.\n\nWeaknesses:  \n- Figure 3 is not cited in the main text. \n- In stage 2, the model is only trained on 10K manually curated videos, which may limit its generalization to real-world scenarios.  \n- Since stage 2 of the method is designed to address failure cases, more failure case analyses should be made to present the limitations of MiniMax-Remover.", "was_revised": true, "final_justification": "Since the author's response and additional experiments answered all my questions, I recommend accepting this paper. The specific reasons are as follows:\n- The authors demonstrate the effectiveness of their method in videos with dynamic backgrounds through additional experiments, acknowledging the challenges existing methods face in handling multiple overlapping objects.\n- The authors further refine their ablation experiments.\n- The authors demonstrate the practicality of using GPT-O3 for evaluation.", "rebuttal": "Thank you very much for your recognition. As you mentioned, our method is *interesting and effective*. The experiments demonstrate that **minimax-remover** generalizes well on both DAVIS and Pexels datasets, as evaluated by both LLMs and human annotators.\n\nMoreover, our **minimax-remover** has been widely embraced by the community. It has been integrated into a popular ComfyUI node by community users, making it easily accessible even to those without a deep learning background. Interestingly, some users have started applying it to produce high-quality videos. Remarkably, it performs well on 1080P videos and can even handle videos with up to 500 frames.\n\n---\n\n***Due to NeurIPS' policy this year prohibiting updates to the pdf and the inclusion of external links, we are unable to put the visualization results in both rebuttal and discussion stage. We sincerely hope for your understanding.***\n\n---\n\n**Q1: Figure 3 is not cited in the main text.**\n\nR1: Thank you for your reminder. We will include a proper citation for this figure in the main text. We really appreciate your helpful suggestion.\n\n&nbsp;\n\n**Q2: In stage 2, the model is only trained on 10K manually curated videos, which may limit its generalization to real-world scenarios.**\n\nR2: Thanks for pointing this problem.\n\n* **In stage-1, our model has been trained on large scale dataset with 2.5 millions of videos.**  Stage 2 can be viewed as the supervised fine-tuning (SFT) stage, similar to that in LLM training. With the usage of minimax optimization, even a small-scale training dataset can lead to significant performance improvements and better alignment with human preferences.\n\n* **Community users have reported it has good generalization in the real world.** Many community users have tried our open-sourced model and reported strong generalization capabilities across a variety of videos. As shown in the table below, applying minimax optimization with only 10K videos yields substantial improvements compared to stage-1 model.\n\n| Method| SSIM |PSNR |TC| VQ| Succ|\n|--------|-----|-----|-----|-----|-----|\n| stage-1|0.9798|35.87|0.9773|6.39|71.11|\n| stage-2|**0.9847**|**36.66** |**0.9776** |**6.48** |**91.11**|\n\nAs shown in the table, the fine-tuned stage-2 model on 10K data significantly improves the successful rate by 20%.\n\n&nbsp;\n\n**Q3: How does the method perform on videos with dynamic backgrounds or multiple overlapping objects?**\n\nR3： Thanks for your comments!\n\n* For Dynamic background, we find that around 90% videos in DAVIS dataset are *not captured with fixed camera position*. Therefore, we selected videos with the dynamic background and evaluate our method and baselines on these videos. The following table shows the evaluation results of our methods and the previous methods.\n\n\n| Method| SSIM |PSNR |TC| VQ| Succ|\n|--------|-----|-----|-----|-----|-----|\n|\tProPainter                |0.9781|35.78|0.9778|5.53|53.09|\n| CoCoCo                    |0.9125|33.26|0.9546|3.29|12.35|\n|\tFloED                     |0.8892|32.75|0.9663|5.07|43.21|\n|\tDiffuEraser               |0.9741|35.12|0.9778|5.68|55.56|\n| VideoComposer             |0.8936|31.31|0.9531|2.13|8.64 |\n| VideoPainter              |0.9652|35.49|0.9623|3.88|19.75|\n| Vace                      |0.8942|32.72|0.9762|2.97|7.41 |\n| MiniMax-Remover (6 steps) |0.9821|36.42|0.9786|6.16|81.48|\n| MiniMax-Remover (12 steps)|0.9823|36.46|0.9787|6.23|86.42|\n| MiniMax-Remover (50 steps)|**0.9828**|**36.48**|**0.9789**|**6.46**|**91.36**|\n\nResults on the above table show that minimax-remover is robust to dynamic background.\n\n* The removal of multiple overlapping objects remains a challenging problem for video object removal systems. It is influenced by several factors, such as the generative capacity of the base model and the complexity of high-quality videos with densely overlapping content. Our minimax-remover demonstrates promising performance on this task, although some failure cases still occur.\n\n&nbsp;\n\n**Q4: In the ablation study, the impact of positive or negative tokens should be further explored, such as removing one and evaluating performance degradation.**\n\nR4: Thanks for your advice. Here we evaluate the impact of positive or negative tokens. The results are shown below:\n\n| Method| SSIM |PSNR |TC| VQ| Succ|\n|--------|-----|-----|-----|-----|-----|\n| stage-1-only-postive-tokens   w/o CFG |0.9764|35.67|0.9769|6.36 |58.89|\n| stage-1-only-negative-tokens  w/o CFG |0.9721|34.66|0.9681|6.10 |15.56|\n| stage-1-both-tokens w/ CFG            |**0.9798**|**35.87**|**0.9773**|**6.39**|**71.11**|\n\nAs indicated in the table, utilizing only positive tokens results in fair performance, whereas relying solely on negative tokens leads to a noticeable performance decline. The incorporation of CFG further improves the model’s performance. These findings highlight the importance of both positive and negative tokens in the stage-1 model.\n\n&nbsp;\n\n**Q5: In line 139, why split the feature into 6 tokens to increase control ability? How to choose the number of tokens?**\n\nThe motivation for using 6 tokens is to enhance the model's controllability. In the attention computation mechanism, employing more control tokens results in greater control ability, as each vision token receives 6 separate control signals. The following presents the evaluation results for different numbers of control tokens.\n\n| Method|Tokens| SSIM |PSNR |TC| VQ| Succ|\n|--------|-----|-----|-----|-----|-----|-----|\n| stage-1 |1         |0.9778|35.82|0.9774|6.13|66.67|\n| stage-1 |2         |**0.9801**|**35.88**|**0.9776**|6.23|70.00|\n| stage-1        |6  |0.9798|35.87|0.9773|6.39|**71.11**|\n| stage-1 |        8 |0.9792|35.85|0.9772|**6.46**|**71.11**|\n\nOverall, using 6 tokens yields a higher success rate compared to using only 1 token, while increasing the number of tokens beyond 6 does not lead to a significant performance improvement.\n\n&nbsp;\n\n**Q6: In line 185, how to select 10K video pairs manually from the 17K generated by the Stage 1 model?**\n\nR6：We asked three human annotators to filter the videos using a voting mechanism. Afterwards, each annotator independently reviewed the videos again to confirm that there were no artifacts, blurriness, or unwanted objects in the masked regions. Together, these two steps ensure the overall quality of the data.\n\n\n&nbsp;\n\n**Q7: In Table 3, the GPT-O3 is used for evaluation. How to verify that large models could replace manual scoring?**\n\nR7: Thank you for your comment. Here, we present the evaluation results for the LLMs. We asked human annotators to select 100 videos, consisting of 50 failure cases and 50 success cases. The evaluation results are shown below.\n\n* Evaluation for different LLMs.\n\n| Success Rate         | GPT-4O| GPT-4O-mini| GPT-4-turbo | GPT-O3    |\n|----------------------|-------|------------|-------------|-----------|\n| Acc in Failure Cases | 94.0% | 98.0%      | 86.0%       | **96.0%** |\n| Acc in Success Cases | 88.0% | 26.0%      | 58.0%       | **94.0%** |\n| Overall Accuracy     | 91.0% | 62.0%      | 72.0%       | **95.0%** |\n\nAs shown in the table, GPT-O3 achieves the best performance, with a 95% consistency with human annotation. This high level of alignment suggests that the decisions made by GPT-O3 can be considered reasonable.\n\n---\n\n\nWe sincerely thank you for your efforts in reviewing our paper. Your valuable comments helps us improve the quality of the manuscript.\n\n**We hope our responses have addressed your concerns and you will reconsider your score accordingly.**"}, {"review_id": "RfDOkNbIBF", "reviewer": "Reviewer_Hp3M", "summary": "This paper proposes a two stage trained video object removal pipeline on DiT backbone. In the first stage, they point out the ambiguity of the text conditioning for object removal and remove the cross attention layers and then train the model using contrastive tokens ( $c^{+}$ for addition, $c^-$ for regeneration) in self attention removing the need for cfg. In the second stage, they further improve the model by distillation to remove time steps and minimax optimization to improve robustness to adversarial input noise which cause failures.", "questions": "- **Contrastive-token validation:** please show evidence showcasing that the tokens really learn distinct “remove” and “regenerate” semantics. Clear proof here would substantially strengthen the paper. Possible evidence can be attention maps, or a quantitative breakdown of removal and hallucination errors without presence of one of the tokens during inference in stage 1. \n\n- **Contrastive-token count:** Author's state using 6 tokens for contrastive c⁺ and c⁻. How does the author's come up with number 6. I assume it is empirical, but no analysis on token count's effect on model performance after stage 1 is provided.   \n\n- **Shadow's** In the supplementary material videos, it is stated that the model's does not remove shadows as they are not included in the mask. Does the model successfully remove both the object and the shadow if they are both included in the mask?\n\n- **Bad-noise search analysis:** Does further optimizing the bad adversarial noise for second stage training improve robustness or does it degrade performance? Also how does the author's come up with the finding that failure cases correlation with specific input noise pattens?\n\n- **Broken Link:** In line 783 of the paper, one reference link is broken which results in a **?** in latex. That can be fixed.", "limitations": "- The checklist marks Broader Impacts as provided in appendix , yet I could not find a dedicated section in the appendix. Can the authors please point to the exact paragraph or expand the discussion to cover potential harms (e.g., erasing evidence, facilitating misinformation).\n\n- More failure cases could be discussed. Currently the article only states the dataset and the gpu consumption of the VAE as a limitation. However,  limitations of the model in terms of failure cases can be discussed further with provided examples.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### **Strengths**:\n- The author's are addressing a valid problem being present in the current literature. Optical flow warping based model's are fast in inference time but most of the time fail with blurry hallucinated artifacts while the Diffusion based method's are prone to object addition and slow. The proposed method is trying to achieve both fast and high quality object removal. \n- The proposed method shows improvements over current literature in terms of metric's such as SSIM, PSNR and TC and authors have conducted a strong literature search for both related work and works to compare. \n- Overall the paper is clear and easy to follow. Motivations for each step are explained and diagrams help with understanding the architecture and training.\n- Second stage adversarial noise based minimax training is an original finding and contrastive tokens for training “remove vs regenerate” inside self-attention is new to object-removal literature.\n\n### **Weaknesses**:\n\n1. **Overall the claims made in the paper should be further and better supported with examples:**\n\n- Contrastive token's semantics are not directly demonstrated. The paper says c⁺ encourages removal and c⁻ discourages regeneration. In table 3 it shows improvements but there is no clear evidence showing whether c⁺ really encourages filling and c⁻ discourages regeneration or not. It might be the case that these tokens are acting in a different behavior and that is not really proved in the paper.  \n\n- Correlation of failure cases after stage 1 with specific input noise pattens, which is the motivation for stage 2 training, needs further in depth explanation. How author's have come up with that finding is not explained. \n\n2.  **Limited visual examples**:\n- Only success cases are shown. Failure cases are described verbally but not illustrated with visual examples.\n- More visual examples on comparisons with other baselines are needed.\n\n3.  **Statistical robustness missing.**\n- All metrics are single-seed; no standard deviations or confidence intervals are reported.", "was_revised": true, "final_justification": "I think this paper solves an open problem in literature with a novel finding. In rebuttal, I have asked the authors to justify Contrastive-token validation and bad noise search analysis and they have addressed my concerns. As a result, I have increased my score.", "rebuttal": "Thank you for your valuable comments. Your suggestions are really appreciated and help us further improve the quality of our submission.\n\nOur method is robust and efficient, and it does not rely on external models to precompute complex conditions. Currently, our Minimax-Remover has been open-sourced and has received a lot of attention from the community. We have received many feedbacks, and some users have even contributed by developing ComfyUI code and integrating our method into a widely used ComfyUI node.\n\n---\n\n***Since NeurIPS 2025 prohibits external links and updated pdfs, we were unable to provide visualization results during both the rebuttal and discussion stages. We hope for your understanding.***\n\n---\n\n**Q1: Contrastive token's semantics are not directly demonstrated. The paper says c⁺ encourages removal and c⁻ discourages regeneration. In table 3 it shows improvements but there is no clear evidence showing whether c⁺ really encourages filling and c⁻ discourages regeneration or not. It might be the case that these tokens are acting in a different behavior and that is not really proved in the paper.**\n\nThank you for your suggestion. Below, we present the comparison results with and without the usage of positive and negative control tokens.\n\n| Method| SSIM |PSNR |TC| VQ| Succ|\n|-----|-----|-----|-----|-----|-----|\n| stage-1-only-postive-tokens   w/o CFG |0.9764|35.67|0.9769|6.36 |58.89|\n| stage-1-only-negative-tokens  w/o CFG |0.9721|34.66|0.9681|6.10 |15.56|\n| stage-1-both-tokens w/ CFG |**0.9798**|**35.87**|**0.9773**|**6.39**|**71.11**|\n\nThe above table shows that using both tokens will have better removal results, while only using positive token is not enough.\n\n&nbsp;\n\n\n**Q2: Correlation of failure cases after stage 1 with specific input noise pattens, which is the motivation for stage 2 training, needs further in depth explanation. How author's have come up with that finding is not explained.**\n\nR2: Thank you for your valuable suggestion. We conducted experiments showing that the random seed significantly affects the success of the stage-1 model. We will include these results in the revised paper. By using three different random seeds to inference our stage-1 model three times, we found that only 65.56% has consistent successful results (all three seeds resulted in successful outcomes).\n\n| Method| Succ|\n|---|---|\n| stage-1-seed-0 |71.11|\n| stage-1 seed-1 |72.22|\n| stage-1 seed-2 |75.56|\n|stage-1 success on all three seeds|65.56|\n\n| Method| SSIM |PSNR |TC| VQ| Succ|\n|---|---|---|---|---|---|\n| stage-1|0.9798|35.87|0.9773|6.39 |71.11|\n| stage-2|**0.9847**|**36.66** |**0.9776**|**6.48** |**91.11**|\n\nAs shown in the table, the stage-1 model is sensitive to random seed, after fine-tuning with the minimax optimization, the performance has significant increase. These findings support our statement: *\"these failure cases are strongly correlated with specific input noise patterns.\"*\n\n&nbsp;\n\n**Q3：Limited visual examples:**\n\n**Q3.1: Only success cases are shown. Failure cases are described verbally but not illustrated with visual examples.**\n\nR3.1: Thanks for your advice. Since the file size limitation of the openreview, we couldn't put  too many cases in main body. We promise that we will put more visual examples in the appendix of our revised paper.\n\n**Q3.2: More visual examples on comparisons with other baselines are needed.**\n\nR3.2: We will put more visual examples with different results of removal models in our revised paper. We promise to put them in the appendix.\n\n&nbsp;\n\n**Q4: Statistical robustness missing.  All metrics are single-seed; no standard deviations or confidence intervals are reported.**\n\nR4: Thanks for your advice. According to your suggestion, we supplied more experiments with different seeds to show the robustness of our minimax-remover. The results are shown in the below table:\n\n| Method| SSIM|PSNR|TC|VQ|Succ|\n|-----|-----|-----|-----|-----|-----|\n| stage-2 seed-0|**0.9847**|36.66 |0.9776|6.48 |91.11|\n| stage-2 seed-1|0.9846|36.62 |0.9772|**6.54** |91.11|\n| stage-2 seed-2|**0.9847**|**36.69** |**0.9788**|6.53 |**92.22**|\n\n| Method| SSIM|PSNR|TC|VQ|Succ|\n|-----|-----|-----|-----|-----|-----|\n|Mean|0.9847|36.66 |0.9779|6.52 |91.48|\n|Std|4.7e-5|0.028 |0.0680|0.026|0.523|\n\nAs shown in the table, the standard variance of success rate is small. It demonstrates that minimax-remover is robust to different random seed.\n\n&nbsp;\n\n**Q5：Contrastive-token validation: please show evidence showcasing that the tokens really learn distinct “remove” and “regenerate” semantics. Clear proof here would strengthen the paper. Possible evidence can be attention maps, or a quantitative breakdown of removal and hallucination errors without presence of one of the tokens during inference in stage 1.**\n\nR5: Thanks for your advice. We highly agree that we should put more experiments in our paper to show the effect of the contrastive tokens. The experiments are shown below:\n\n| Method| SSIM |PSNR |TC| VQ| Succ|\n|-----|-----|-----|-----|-----|----|\n| stage-1-only-postive-tokens w/o CFG |0.9764|35.67|0.9769|6.36 |58.89|\n| stage-1-only-negative-tokens w/o CFG |0.9721|34.66|0.9681|6.10 |15.56|\n| stage-1-both-tokens w/ CFG |**0.9798**|**35.87**|**0.9773**|**6.39** |**71.11**|\n\nThe results in the table indicate that the combination of positive and negative control tokens yields improved removal performance.\n\n*Since NeurIPS prohibits external links and updated pdfs during the rebuttal and discussion stages, visualization results such as attention maps will be included in the appendix of our next manuscript version.* \n\n&nbsp;\n\n**Q6: Contrastive-token count**\n\n**Q6.1:  Author's state using 6 tokens for contrastive c⁺ and c⁻. How does the author's come up with number 6.**\n\nR6.1: Thank you for your suggestion. \n\n* Our intention in using 6 control tokens instead of 1 token is to enhance the model’s controllability. In the attention mechanism, using more control tokens increases the overall control weight, as each vision token receives multiple control signals. The experimental results are shown below.\n\n| Method|Tokens| SSIM |PSNR |TC| VQ| Succ|\n|-----|-----|-----|-----|-----|-----|-----|\n| stage-1 |1|0.9778|35.82|0.9774|6.13|66.67|\n| stage-1 |2|**0.9806**|**35.88**|**0.9776**|6.23|70.00|\n| stage-1 |6|0.9798|35.87|0.9773|6.39|**71.11**|\n| stage-1 |8|0.9797|35.85|0.9772|**6.46**|**71.11**|\n\nAs shown in the table, using 6 tokens is sufficient to achieve effective control in the removal process. Increasing the number of tokens to 8 does not bring in more improvement.\n\n**Q6.2: no token count's effect on model performance after stage 1 is provided.**\n\nR6.2: We use contrastive tokens only in stage-1. Since stage-2 does not rely on CFG, we remove the contrastive tokens during this stage.\n\n&nbsp;\n\n**Q7: Shadow's in the supplementary material videos. Does the model successfully remove both the object and the shadow if they are both included in the mask?**\n\nR7: Yes, our model is capable of handling such cases. If masks are provided for both the object and its shadow, our model can successfully remove both components. We noticed that community users have provided many successful cases that remove both object and shadow. Below, we present more results of our model on multi-object removal experiments.\n\n| Method| SSIM |PSNR |TC| VQ| Succ|\n|-----|-----|-----|-----|-----|-----|\n|ProPainter |0.9781|35.78|0.9778|5.53|53.09|\n| CoCoCo|0.9125|33.26|0.9546|3.29|12.35|\n|FloED|0.8892|32.75|0.9663|5.07|43.21|\n|DiffuEraser|0.9741|35.12|0.9778|5.68|55.56|\n|VideoComposer|0.8936|31.31|0.9531|2.13|8.64 |\n|VideoPainter|0.9652|35.49|0.9623|3.88|19.75|\n|Vace |0.8942|32.72|0.9762|2.97|7.41 |\n|MiniMax-Remover (6 steps) |0.9821|36.42|0.9786|6.16|81.48|\n|MiniMax-Remover (12 steps)|0.9823|36.46|0.9787|6.23|86.42|\n|MiniMax-Remover (50 steps)|**0.9828**|**36.48**|**0.9789**|**6.46**|**91.36**|\n\n&nbsp;\n\n**Q8: Bad-noise search analysis** \n\n**Q8.1: Does further optimizing the bad adversarial noise for stage-2 training improve robustness or degrade performance?**\n\nR8.1: In our earlier experiments, we attempted to use a two-step optimization process to generate adversarial noise. While this approach requires more time to converge, it did not lead to noticeable improvements in visual results.\n\n**Q8.2: How does you find that failure cases correlation with specific input noise pattens?**\n\nR8.2: During stage-1 evaluation, we observed that changing the random seed often flipped failure cases into successes and vice versa. This frequent phenomenon suggests that the outcome of removal is highly sensitive to input noise.*. \n\nThis observation inspired us to further investigate the characteristics of **\"bad\" noise**. To address its adverse effects, we proposed a **minimax optimization strategy** to mitigate the impact of such noise. The results obtained in stage-2 significantly outperform those in stage-1, which further supports our claim. *The experimental results can be referred to our response to Q2.*\n\n&nbsp;\n\n**Q9: Broken Link: In line 783 of the paper, one reference link is broken which results in a ? in latex. That can be fixed.**\n\nR9: Thanks for pointing out this typo. We apologize for the mistake and will correct it in the revised paper.\n\n&nbsp;\n\n**Q10: Disscussing broader impacts in appendix.**\n\nR10: We appreciate your valuable suggestion. A discussion on the broader impacts will be added in the revised version of the paper.\n\n&nbsp;\n\n**Q11: More failure cases could be discussed. The failure cases can be discussed further with provided examples.**\n\nR11: Thanks for your advice. We will put failure examples in the appendix. However, due to the policy of NeurIPS, we couldn't provide links or renewed pdf to show the visualization results. We sincerely hope your understanding.\n\n---\n\n**We really hope our response has resolved your concerns, and we would greatly appreciate it if you could consider increasing your score.**"}]

Author rebuttal:

Meta-review: This paper proposes a two-stage method for video object removal. All the reviewers opine that the paper should be accepted and thus I am recommending acceptance.
Decision: Accept (poster)