Semantically Consistent Video Inpainting with Conditional Diffusion Models

## Abstract

Abstract Current state-of-the-art methods for video inpainting typically rely on optical flow or attention-based approaches to inpaint masked regions by propagating visual information across frames. While such approaches have led to significant progress on standard benchmarks, they struggle with tasks that require the synthesis of novel content that is not present in other frames. In this paper, we reframe video inpainting as a conditional generative modeling problem and present a framework for solving such problems with conditional video diffusion models. We introduce inpainting-specific sampling schemes which capture crucial long-range dependencies in the context, and devise a novel method for conditioning on the known pixels in incomplete frames. We highlight the advantages of using a generative approach for this task, showing that our method is capable of generating diverse, high-quality inpaintings and synthesizing new content that is spatially, temporally, and semantically consistent with the provided context.

## 1 Introduction

Video inpainting is the task of filling in missing pixels in a video with plausible values. It has practical applications in video editing and visual effects, including video restoration (Tang et al., ), object or watermark removal (Patwardhan et al., ), and video stabilization (Matsushita et al., ). While significant progress has been made on image inpainting in recent years (Saharia et al., ; Lugmayr et al., ; Suvorov et al., ), video inpainting remains a challenging task due to the added time dimension, which drastically increases computational complexity and leads to a stricter notion of what it means for an inpainting to be plausible. Specifically, inpainted regions require not only per-frame spatial and semantic consistency with the context as in image inpainting, but also temporal consistency between frames and realistic motion of objects in the scene.

Current state-of-the-art video inpainting methods explicitly attempt to inpaint masked regions by exploiting visual information present in other frames, typically by using optical flow estimates (Li et al., ; Gao et al., ) or attention-based approaches (Zeng et al., ; Liu et al., ) to determine how this information should be propagated across frames. Such methods, which we refer to as “content propagation” methods, implicitly assume that the content in unmasked regions is sufficient to fill in the missing values, and tend to fail when this assumption is violated. In the presence of large occlusions, objects may be partially or fully occluded for long durations, and in these cases an inpainting method may need to complete an object’s appearance (as in Fig. 1) or infer its behaviour (as in Fig. 2) to produce a plausible result, requiring the generation of novel content.

Figure: Figure 2: An example inpainting task from our Traffic-Scenes dataset. The black car becomes occluded near the top of the roundabout and emerges many frames later near the bottom. Inpainting such an example requires the ability to model plausible vehicle behaviour.
Refer to caption: x2.png

We argue that a more sensible approach to video inpainting is to learn a conditional distribution over possible inpaintings given the observed context. Conditional generative models such as GANs (Suvorov et al., ), autoregressive models (Peng et al., ), and diffusion models (Saharia et al., ; Lugmayr et al., ) have long dominated image inpainting, a task that inherently requires content synthesis as there is no additional information to draw upon. Learning such a distribution naturally accounts ill-posedness of the video inpainting problem: for instance, given the observed entry and exit points of the car in Fig. 2 there exists a diversity of plausible trajectories it could take. Further, generative approaches for image inpainting have a demonstrated ability to produce results that are *semantically* consistent with the context, a quality which is particularly important for video inpainting tasks that require inferring an object’s behaviour as in Fig. 2 – without a strong prior, there is insufficient information in the context to determine what such a trajectory might look like; the model must have some notion of what makes a vehicle trajectory plausible at a semantic level.

In this work, we present a framework for using conditional video diffusion models for video inpainting. We demonstrate how to use long-range temporal attention to generate semantically consistent behaviour of inpainted objects over long time horizons, even when our model cannot be jointly model all frames due to memory constraints. We can do this even for inpainted objects that have limited or no visibility in the context, a quality not present in the current literature. We report strong experimental results on several challenging video inpainting tasks, outperforming state-of-the-art approaches on a range of standard metrics.

## 2 Related Work

Video Inpainting. Recent advances in video inpainting have largely been driven by methods which fill in masked regions by borrowing content from the unmasked regions of other frames, which we refer to as content propagation methods. These methods typically use optical flow estimates (Huang et al., ; Kim et al., ; Xu et al., ; Gao et al., ), self-attention (Zeng et al., ; Liu et al., ; Oh et al., ; Lee et al., ) or a combination of both (Zhou et al., ; Zhang et al., ; Li et al., ) to determine how to propagate pixel values or learned features across frames. Such methods often produce visually compelling results, particularly on tasks where the mask-occluded region is visible in nearby frames such as foreground object removal with a near-static background. They struggle, however, in cases where this does not hold, for instance in the presence of heavy camera motion, large masks, or tasks where semantic understanding of the video content is required to produce a convincing result.

More recent work has utilized diffusion models for video inpainting. Gu et al. () proposes a method for video inpainting that combines a video diffusion model with optical flow guidance, following a similar “content propagation” approach to the methods listed above. Chang et al. () uses a latent diffusion model (Rombach et al., ; Vahdat et al., ) to remove the agent’s view of itself from egocentric videos for applications in robotics. Notably, this is framed as an image inpainting task, where the goal is to remove the agent (with a mask provided by a segmentation model) from a single video frame conditioned on $h$ previous frames. Consequently, the results lack temporal consistency when viewed as videos, and the model is evaluated using image inpainting metrics only. Zhang et al. () proposes a method for the related task of text-conditional video inpainting, which produces impressive results but requires user intervention.

Image Inpainting with Diffusion Models. This work takes inspiration from the recent success of diffusion models for image inpainting. These methods can be split into two groups: those that inpaint using an unconditional diffusion model by making heuristic adjustments to the sampling procedure (Lugmayr et al., ; Zhang et al., ), and those that explicitly train a conditional diffusion model which, if sufficiently expressive and trained to optimality, enables exact sampling from the conditional distribution (Saharia et al., ). In this work we extend the latter approach to video data.

## 3 Background

Conditional Diffusion Models. A conditional diffusion model (Tashiro et al., ; Ho et al., ; Sohl-Dickstein et al., ) is a generative model parameterized by a neural network trained to remove noise from data. The network is conditioned on $t\in\{1,\ldots,T\}$, an integer describing how much noise has been added to the data. Given hyperparameters $1>\bar{\alpha}_{1}>\ldots>\bar{\alpha}_{T}>0$, training data is created by multiplying the original data by a factor $\sqrt{\bar{\alpha}_{t}}$ and then adding unit Gaussian noise $\bm{\epsilon}$ scaled to have variance $(1-\bar{\alpha}_{t}^{2})$. The network should then map from this noisy data, the timestep $t$, and conditioning input $\mathbf{y}$, to a prediction of the added noise $\bm{\epsilon}$. It is trained with the squared error loss

$$ $\mathcal{L}_{\text{CDM}}(\theta):=\mathbb{E}_{t,\mathbf{x},\mathbf{y},\bm{ \epsilon}}\left[\left\|\bm{\epsilon}-\bm{\epsilon}_{\theta}\left(\sqrt{\bar{ \alpha}_{t}}\mathbf{x}+\sqrt{1-\bar{\alpha}_{t}}\bm{\epsilon},\mathbf{y},t \right)\right\|^{2}\right],$ (1) $$

where $\bm{\epsilon}_{\theta}(\ldots)$ is the output of the network. Data $\mathbf{x}$ and $\mathbf{y}$ are sampled from the data distribution $p_{\text{data}}$, and the timestep $t$ is typically sampled from a pre-specified categorical distribution. Once such a network has been trained, various methods exist for using it to draw approximate samples from $p_{\text{data}}(\mathbf{x}|\mathbf{y})$ (Ho et al., ; Sohl-Dickstein et al., ; Tashiro et al., ; Song et al., ; Karras et al., ).

Figure: (a) AR
Refer to caption: extracted/5911903/figures/sampling-scheme-visualizations/autoreg.png

Video Diffusion Models. A number of recent papers have proposed diffusion-based approaches to generative modeling of video data (Höppe et al., ; Harvey et al., ; Ho et al., ; Yang et al., ; Voleti et al., ).
We follow the majority of these approaches in using a 4-D U-Net (Ronneberger et al., ) architecture to parameterize $\bm{\epsilon}_{\theta}(\ldots)$.
Alternating spatial and temporal attention blocks within the U-Net capture dependencies within and across frames respectively, with relative positional encodings (Shaw et al., ; Wu et al., ) providing information about each frame’s position within the video.
Due to computational constraints, video diffusion models are inherently limited to conditioning on and generating a small number of frames at a time, which we denote as $K$.

Flexible Video Diffusion Models. Generating long videos with frame count $N\gg K$ then requires sampling from the diffusion model multiple times. A typical approach is to generate frames in multiple stages in a block-autoregressive fashion, in each stage sampling $K/2$ frames conditioned on the previous $K/2$ frames. We depict this approach in Fig. 3(a), with each row representing one stage. A problem with this strategy is that it fails to capture dependencies on frames more than $K/2$ frames in the past.
Alternative orders in which to generate frames (which we refer to as “sampling schemes”) are possible, as depicted in Figs. 3(c) and 3(b). To avoid iteratively retraining to identify an optimal sampling scheme,
Harvey et al. () proposes training a single model that can perform well using any sampling scheme, referred to as a Flexible Diffusion Model (FDM). An FDM is trained to generate any subset of video frames conditioned on any other subset with the objective

$$ $\mathcal{L}_{\text{FDM}}(\theta):=\mathbb{E}_{t,\mathbf{x},\mathbf{y},\mathcal {X},\mathcal{Y},\bm{\epsilon}}\left[\left\|\bm{\epsilon}-\bm{\epsilon}_{\theta }\left(\sqrt{\bar{\alpha}_{t}}\mathbf{x}+\sqrt{1-\bar{\alpha}_{t}}\bm{\epsilon },\mathbf{y},\mathcal{X},\mathcal{Y},t\right)\right\|^{2}\right],$ (2) $$

in which $\mathbf{x}$ are the frames to remove noise from at indices $\mathcal{X}$, and $\mathbf{y}$ are the frames to condition on at indices $\mathcal{Y}$.
Sampling $\mathbf{x}$ and $\mathbf{y}$ is accomplished by first sampling $\mathcal{X}$ and $\mathcal{Y}$, then sampling a training video, and then extracting the frames at indices $\mathcal{X}$ and $\mathcal{Y}$ to form $\mathbf{x}$ and $\mathbf{y}$, respectively.
Once a network is trained to make predictions for arbitrary indices $\mathcal{X}$ given arbitrary indices $\mathcal{Y}$, it can be used to sample videos with any desired sampling scheme. Sampling schemes will henceforth be denoted as $\{\mathcal{X}_{s},\mathcal{Y}_{s}\}_{s=1}^{S}$ where $S$ is the number of sampling stages and, at stage $s$, $\mathcal{X}_{s}$ and $\mathcal{Y}_{s}$ are the indices of the latent and observed frames, respectively.

## 4 Video Inpainting with Conditional Diffusion Models

### 4.1 Problem Formulation

We consider the problem of creating an $N$-frame video $\mathbf{V}$ conditioned on some subset of known pixel values specified by a pixel-level mask $\mathbf{M}$. Entries of $\mathbf{M}$ take value $1$ where the corresponding pixel in $\mathbf{V}$ is known and take 0 0 elsewhere. Unlike content propagation methods which deterministically generate one possible completion, we aim to model the posterior distribution over possible inpaintings as $p_{\theta}(\mathbf{V}|\mathbf{V}\odot\mathbf{M})\approx p_{\text{data}}(
\mathbf{V}|\mathbf{V}\odot\mathbf{M})$ with a conditional video diffusion model, where $\odot$ is defined such that $a\odot\mathbf{M}$ returns the elements in $a$ for which the corresponding value in $\mathbf{M}$ is 1.

Recall that, due to memory constraints, video diffusion models are typically restricted to conditioning on or generating at most $K$ frames at a time. In the video inpainting problem we are predicting and conditioning on pixels rather than frames, but our network architecture imposes an analogous constraint: we can only predict or condition on pixels from at most $K$ different frames at a time. We modify the definition of a sampling scheme from FDM (Harvey et al., ) as follows: we again denote sampling schemes $\{\mathcal{X}_{s},\mathcal{Y}_{s}\}_{s=1}^{S}$ with $\mathcal{X}_{s}$ and $\mathcal{Y}_{s}$ being collections of frame indices. Now, however, at each stage we sample values for only unknown pixels in frames indexed by $\mathcal{X}_{s}$, and condition on known pixel values in all frames indexed by either $\mathcal{X}_{s}$ or $\mathcal{Y}_{s}$. Referencing Fig. 5, in each row (stage) we show frames indexed by $\mathcal{X}_{s}$ in cyan and frames indexed by $\mathcal{Y}_{s}$ in either dark red or bright red. Frames shown in bright red contain missing pixels, which we do not wish to sample or condition on until a later sampling stage; we describe how we deal with this in Section 4.3.

### 4.2 Method

Architecture. We generalize the FDM architecture to use pixel-level masks rather than frame-level masks, as in the image inpainting approach proposed by Saharia et al. (). Concretely, every input frame is concatenated with a mask which takes value $1$ where the corresponding pixel is observed and 0 0 elsewhere. The input values are clean (without added noise) for observed pixels and noisy otherwise.

Model Inputs. Recall that we wish to train a model that can generate plausible values for unknown pixels in frames indexed by $\mathcal{X}$, conditioned on known pixel values in frames indexed by either $\mathcal{X}$ or $\mathcal{Y}$. We simulate such tasks by first sampling a video $\mathbf{V}$ and a mask $\mathbf{M}$ from our dataset, and then sampling frame indices $\mathcal{X}$ and $\mathcal{Y}$ from a “frame index distribution” similar to that of FDM.(^1^11Our frame index distribution is a mixture distribution between the one used by FDM and one which always samples $\mathcal{X}$ and $\mathcal{Y}$ so that they represent sequences of consecutive frames. We found that including these sequences of consecutive frames improved temporal coherence.)
The distribution over masks $\mathbf{M}$ can be understood as reflecting the variability in the types of masks we will encounter at test-time, and the frame index distribution can be understood as reflecting our desire to be able to sample from the model using arbitrary sampling schemes. Given $\mathbf{M}$, $\mathcal{X}$, and $\mathcal{Y}$, we create a combined list of frames $\mathcal{X}\oplus\mathcal{Y}$, where $\oplus$ denotes concatenation, and a corresponding mask $\mathbf{M}_{\mathcal{X},\mathcal{Y}}:=\mathbf{M}[\mathcal{X}]\oplus\mathbbm{1}
[\mathcal{Y}]$, where $\mathbbm{1}[\mathcal{Y}]$ is a mask of all $1$’s for each frame indexed in $\mathcal{Y}$. This masks only the missing pixels in frames $\mathcal{X}$ while treating all pixels in frames $\mathcal{Y}$ as observed (visualized in Fig. 4).
We then extract our training targets from the video as $\mathbf{x}:=\mathbf{V}[\mathcal{X}\oplus\mathcal{Y}]\odot(1-\mathbf{M}_{
\mathcal{X},\mathcal{Y}})$, and our observations as $\mathbf{y}:=\mathbf{V}[\mathcal{X}\oplus\mathcal{Y}]\odot\mathbf{M}_{\mathcal{
X},\mathcal{Y}}$.

Training Objective. Sampling $\mathbf{V}$, $\mathbf{M}$, $\mathcal{X}$, and $\mathcal{Y}$ for every training example therefore defines a distribution over $\mathbf{x}$ and $\mathbf{y}$, which we use when estimating the expectation over them in Eq. 2. Combining this method of sampling $\mathbf{x}$ and $\mathbf{y}$ with our pixel-wise mask, we write the loss as

$$ $\mathcal{L}(\theta):=\mathbb{E}_{t,\mathbf{x},\mathbf{y},\mathcal{X},\mathcal{ Y},\bm{\epsilon}}\left[(\mathbf{1}-\mathbf{M}_{\mathcal{X},\mathcal{Y}})\odot \left\|\bm{\epsilon}-\bm{\epsilon}_{\theta}\left(\sqrt{\bar{\alpha}_{t}} \mathbf{x}+\sqrt{1-\bar{\alpha}_{t}}\bm{\epsilon},\mathbf{y},\mathbf{M}_{ \mathcal{X},\mathcal{Y}},\mathcal{X},\mathcal{Y},t\right)\right\|^{2}\right],$ (3) $$

where $\mathcal{X},\mathcal{Y}$ provide information about each frame’s index within $\mathbf{V}$, and $\mathbf{M}_{\mathcal{X},\mathcal{Y}}$ is the mask. Note that the loss is only computed for missing pixels, as indicated by the elementwise multiplication of the loss by $(\mathbf{1}-\mathbf{M}_{\mathcal{X},\mathcal{Y}})$.

Sampling. Sampling from a model trained using the above objective can be done with any diffusion model sampler. The only difference is that, at each step in the reverse process, only the values of the unknown pixels are updated, such that the known pixels retain their ground-truth values at each step. We use the Heun sampler proposed by Karras et al. (), and leave further details to the appendix.

Figure: Figure 4: Example model inputs during training. Left: Visualizations of a 5-frame video $\mathbf{V}$, a corresponding mask $\mathbf{M}$, and the resulting known pixel values $\mathbf{V}\odot\mathbf{M}$. Center: Collated training inputs if $\mathcal{X}=[0,3]$ and $\mathcal{Y}=[2]$. The observations in $\mathbf{y}$ are then the whole of frame 2 and known pixel values in frames 0 and 3. The task is to predict the unknown pixel values in frames 0 and 3. Right: Inputs fed to the neural network, with noise added to pixel values in $\mathbf{x}$ but not those in $\mathbf{y}$. The task is then to predict the noise $\epsilon$. For simplicity we do not show inputs $t$, $\mathbf{M}_{\mathcal{X},\mathcal{Y}}$, or $\mathcal{X}\oplus\mathcal{Y}$.
Refer to caption: x3.png

Figure: (a) Lookahead-AR.
Refer to caption: extracted/5911903/figures/sampling-scheme-visualizations/autoregressive-with-near-future.png

### 4.3 Conditioning on Incomplete Frames

Given our proposed architecture and training procedure, we can sample from the resulting models using the FDM sampling schemes shown in Fig. 3 without further complication. A downside of these sampling schemes, however, is that they do not enable us to condition on frames with unknown pixel values. That is, we are unable to condition on the known pixels in a frame unless we either (a) have previously inpainted it and know all of its pixel values already, or (b) are inpainting it as we condition on it. We show in our experiments that this often leaves us unable to account for important dependencies in the context.

We therefore propose a method for conditioning on incomplete frames. This enables the sampling schemes shown in Fig. 5, where we condition on the known pixels in incomplete frames, marked in red. Recall that $\mathbf{x}$ denotes “unknown pixels in frames indexed by $\mathcal{X}$” and $\mathbf{y}$ denotes “known pixels in frames indexed by $\mathcal{X}$ or $\mathcal{Y}$”. If any frames indexed by $\mathcal{Y}$ are incomplete then we have a third category, which we’ll call $\mathbf{z}$: “unknown pixels in frames indexed by $\mathcal{Y}$”.

We then wish to approximately sample $\mathbf{x}\sim p_{\text{data}}(\cdot|\mathbf{y})$ without requiring values of $\mathbf{z}$. We do not have a way to sample directly from an approximation of this distribution, as the diffusion model is not trained to condition on “incomplete” frames. We note, however, that this desired distribution is the marginal of a distribution that our diffusion model can approximate:

$$ $p_{\text{data}}(\mathbf{x}|\mathbf{y})=\int p_{\text{data}}(\mathbf{x},\mathbf {z}|\mathbf{y})\mathrm{d}\mathbf{z}\approx\int p_{\theta}(\mathbf{x},\mathbf{z }|\mathbf{y})\mathrm{d}\mathbf{z}.$ (4) $$

This means that we can sample from the required approximation of $p_{\text{data}}(\mathbf{x}|\mathbf{y})$ by sampling from $p_{\theta}(\mathbf{x},\mathbf{z}|\mathbf{y})$ and then simply discarding $\mathbf{z}$.

### 4.4 Sampling Schemes for Video Inpainting

The ability to condition on incomplete frames enables us to design new sampling schemes that better capture dependencies that are necessary for high-quality video inpainting. Lookahead-AR is a variant of AR that takes into account information from future frames by conditioning on the observed parts of the frames immediately after the sequence of frames being generated, as well as on the frames before; see Fig. 5(a). Lookahead-AR++ builds on “Lookahead-AR” by conditioning on the observed parts of frames far in the future instead of of frames immediately after those being sampled; see Fig. 5(b).
Multires-AR-3 builds on “Lookahead-AR” by first inpainting every fifteenth frame using Lookahead-AR, then inpainting every fifth frame while conditioning on nearby inpainted frames, and then inpainting all other frames. We visualize a “Multires-AR-2” version (inpainting every third frame and then every frame) in Fig. 5(c). Visualizations of all sampling schemes considered in this work can be found in Appendix C.

## 5 Experiments

Figure: Figure 6: Inpaintings from our model and ProPainter on the task introduced in Fig. 2 from our Traffic-Scenes dataset. Our model can inpaint a realistic trajectory for the occluded vehicle. Competing flow-based approaches correctly inpaint the background but are unable to account for the vehicle.
Refer to caption: x4.png

### 5.1 Datasets

To highlight the unique capabilities our generative approach offers, we wish to target video inpainting tasks in which visual information in nearby frames cannot be easily exploited to achieve a convincing result. The YouTube-VOS (Xu et al., ) (training and test) and DAVIS (Perazzi et al., ) (test) video object segmentation datasets have become the *de facto* standard benchmark datasets for video inpainting in recent years, and the foreground object masks included in these datasets have led to a heavy focus on object removal tasks in qualitative evaluations of video inpainting methods. Object removal in these datasets is a task to which content propagation methods are specifically well-suited, as the backgrounds are often near-stationary and so information in neighboring frames can be used to great effect. In contrast, we wish to focus on tasks where inpainting requires the visual appearances of objects to be hallucinated in whole or in part and realistically propagated through time, or where the behavior of occluded objects must be inferred. We propose three new large-scale video inpainting datasets targeting such tasks. All datasets are 256 $\times$ 256 resolution and 10 fps. Representative examples of each dataset, along with details of how each dataset was constructed, are included in Appendix D.

BDD-Inpainting. We adapt the BDD100K (Yu et al., ) dataset for video inpainting. This dataset contains 100,000 high-quality first-person driving videos and includes a diversity of geographic locations and weather conditions. We select a random subset of approximately 50,000
of these, and generate a set of both moving and stationary masks of four types: grids, horizontal or vertical lines, boxes (Fig. 6), and blobs (Fig. 1). During training both videos and masks are sampled uniformly, giving a distribution over an extremely varied set of inpainting tasks. We include two test sets of 100 video-mask pairs each, the first (BDD-Inpainting) containing only grid, line, and box masks, and the second (BDD-Inpainting-Blobs) containing only blob masks. The second is a substantially more challenging test set, as the masks are often large and occlude objects in the scene for a significant period of time. See the appendix for representative examples; mask types, shown in green, are indicated for each. The test sets we use, along with code for processing the original BDD100K dataset and generating masks, will be released upon publication.

Figure: Figure 7: Inpaintings from our model on the Inpainting-Cars dataset. The first row is the masked input to the model, the lower two rows are two separate inpaintings sampled from our model.
Refer to caption: x5.png

Inpainting-Cars. We use an in-house dataset of overhead drone footage of vehicle traffic to create a dataset targeting the *addition* of cars to videos.
Crops are taken such that each video centers a single vehicle, and tracker-generated
bounding boxes are used as masks. Note that the masked-out vehicle is not visible at any time in the context, and so, given an input, the model must generate a vehicle and propagate it through time, using only the size and movement of the mask as clues to plausible vehicle appearance and motion.

Traffic Scenes. Using the same in-house dataset as referenced above, we create another dataset where large sections of the roadway are occluded, requiring the model to infer vehicle behavior in the masked-out region. Crops are centered over road features like intersections, roundabouts, highway on-ramps, *etc*. where complex vehicle behavior and interactions take place. This dataset contains exceptionally challenging video inpainting tasks as vehicles are often occluded for long durations of time, requiring the model to generate plausible trajectories that are semantically consistent with both the observations (e.g. the entry and exit point of a vehicle from the masked region) and the roadway. This dataset contains approximately 13,000 videos in the training set and 100 video-mask pairs in the test set.

### 5.2 Baselines and Metrics

We compare our method with four recently proposed video inpainting methods that achieve state-of-the-art performance on standard benchmarks: ProPainter (Zhou et al., ), E^2FGVI (Li et al., ), FGT (Zhang et al., ), and FGVC (Gao et al., ). For each model, we use pre-trained checkpoints made available by the respective authors. We adopt the evaluation suite of DEVIL (Szeto & Corso, ), which includes a comprehensive selection of commonly used metrics targeting three different aspects of inpainting quality:

- •
Reconstruction, or how well the method’s output matches the ground truth: PSNR, SSIM (Wang et al., ), LPIPS (Zhang et al., ), PVCS (Szeto & Corso, ).
- •
Perceptual realism, or how well the appearance and/or motion resembles a reference set of ground truth videos: FID (Heusel et al., ), VFID (Wang et al., ).
- •
Temporal consistency: Flow warping error ($E_{\text{warp}}$) (Lai et al., ), which measures how well an inpainting follows the optical flow as calculated on the ground truth.

For our method, we train one model on each dataset with $K=16$. Models are trained on 4$\times$ NVIDIA A100 GPUs for 1-4 weeks. A detailed accounting of each model’s hyperparameters and training procedure can be found in the appendix.

**Table 1: Quantitative comparison with state-of-the-art video inpainting methods across each of our four datasets. For each dataset/metric, the best performing model is indicated with bold font and the second best performing model is underlined. Lookahead-AR++ is used for all datasets.**
| Method | PSNR$\blacktriangle$ | SSIM$\blacktriangle$ | LPIPS$\blacktriangledown$ | PVCS$\blacktriangledown$ | FID$\blacktriangledown$ | VFID$\blacktriangledown$ | $E_{\text{warp}}$$\blacktriangledown$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| BDD-Inpainting |  |  |  |  |  |  |  |
| ProPainter | 32.65 | 0.968 | 0.0355 | 0.2806 | 2.51 | 0.1599 | $\mathbf{\,1.5{\cdot}10^{\shortminus 3}}$ |
| FGT | 28.50 | 0.928 | 0.0843 | 0.6430 | 9.94 | 0.7863 | $\,4.0{\cdot}10^{\shortminus 3}$ |
| E^2FGVI | 30.16 | 0.946 | 0.0640 | 0.4765 | 6.58 | 0.3665 | $\,2.3{\cdot}10^{\shortminus 3}$ |
| FGVC | 25.84 | 0.884 | 0.1498 | 1.0210 | 32.08 | 1.7995 | $\,7.5{\cdot}10^{\shortminus 3}$ |
| Ours | 33.68 | 0.972 | 0.0261 | 0.2037 | 1.71 | 0.0748 | $\underline{1.8{\cdot}10^{\shortminus 3}}$ |
| BDD-Inpainting-Blobs |  |  |  |  |  |  |  |
| ProPainter | 30.54 | 0.960 | 0.0467 | 0.3120 | 2.56 | 0.1499 | $\mathbf{\,1.2{\cdot}10^{\shortminus 3}}$ |
| FGT | 27.33 | 0.938 | 0.0737 | 0.5130 | 9.41 | 0.3594 | $\,2.5{\cdot}10^{\shortminus 3}$ |
| E^2FGVI | 29.04 | 0.950 | 0.0667 | 0.4414 | 4.23 | 0.2339 | $\,1.7{\cdot}10^{\shortminus 3}$ |
| FGVC | 25.10 | 0.913 | 0.0980 | 0.6957 | 16.64 | 0.6184 | $\,3.8{\cdot}10^{\shortminus 3}$ |
| Ours | 30.67 | 0.961 | 0.0442 | 0.2857 | 1.69 | 0.1083 | $\,\underline{1.5{\cdot}10^{\shortminus 3}}$ |
| Traffic-Scenes |  |  |  |  |  |  |  |
| ProPainter | 31.98 | 0.967 | 0.0326 | 0.2909 | 8.51 | 0.3482 | $\,\underline{2.5{\cdot}10^{\shortminus 4}}$ |
| FGT | 31.97 | 0.963 | 0.0397 | 0.3528 | 9.92 | 0.5392 | $\,3.6{\cdot}10^{\shortminus 4}$ |
| E^2FGVI | 31.40 | 0.957 | 0.0440 | 0.3809 | 13.13 | 0.6113 | $\,4.1{\cdot}10^{\shortminus 4}$ |
| FGVC | 29.11 | 0.926 | 0.0794 | 0.5914 | 25.93 | 1.2358 | $\,9.9{\cdot}10^{\shortminus 4}$ |
| Ours | 35.29 | 0.978 | 0.0202 | 0.1725 | 4.87 | 0.1637 | $\mathbf{\,2.3{\cdot}10^{\shortminus 4}}$ |

### 5.3 Quantitative Evaluation

We report quantitative results for three of our datasets in Table 1. For each dataset, for our method we report metrics for the best-performing model and sampling scheme we found for that dataset. Our method outperforms the baselines on all metrics except $E_{\text{warp}}$ for all datasets, often by a significant margin. We suspect the discrepancy between $E_{\text{warp}}$ and the other metrics arises because each of the competing methods predicts a completion of the optical flow field and utilizes this during the inpainting process, in a sense explicitly targeting $E_{\text{warp}}$.
Inpainting-Cars is omitted from this section, as we are not aware of an existing method suitable for this task.

### 5.4 Qualitative Evaluation

BDD-Inpainting. On this dataset, qualitative differences between our method and competing approaches are most evident in the presence of large masks that partially or fully occlude objects for a long period of time. In such cases our method can retain or complete the visual appearance of occluded objects and propagate them through time in a realistic way, while such objects tend to disappear gradually with flow-based approaches (as in Fig. 1). Qualitative results on this dataset are heavily influenced by the sampling scheme used. Our “Lookahead-AR++” sampling scheme tends to perform best, as it allows information from both past and future frames to be conditioned on in the inpainting process. See Section F.2 for a qualitative demonstration of the effects of different sampling schemes. Additional results are provided in Section H.1.

Traffic-Scenes. On this dataset, all competing methods tend to inpaint only the road surface; when vehicles enter the occluded region they disappear almost instantaneously. Our method shows a surprising ability to inpaint long, realistic trajectories that are consistent with the entry/exit points of vehicles into the occluded region, as well as correctly following roadways as in Fig. 6. Additional results are provided in Section H.2.

Inpainting-Cars. Sampled inpaintings from our model on an example from the Inpainting-Cars dataset are shown in Fig. 7. Our model is capable of generating diverse, visually realistic inpaintings for a given input. We note the model’s ability to use *semantic* cues from the context to generate plausible visual features, such as realistic behavior of shadows and plausible turning trajectories based only on the movement of the mask. Additional results are provided in Section H.3.

### 5.5 Ablations

Effect of Sampling Schemes. We compare selected sampling schemes on the Traffic-Scenes dataset in Table 2, before providing more thorough qualitative and quantitative comparisons in the appendix. We find Lookahead-AR++ performs best on all datasets in terms of video realism (VFID) and temporal consistency (warp error), demonstrating the importance of our method’s ability to condition on incomplete frames. AR tends to do poorly on all metrics on both the Traffic-Scenes and BDD-Inpainting datasets; this is likely due to its inability to account for observed pixel values in frames coming after the ones being generated at each stage, causing divergence from the ground-truth and then artifacts when producing later frames conditioned on incompatible values. Hierarchy-2 does well on some metrics of reconstruction and frame-wise perceptual quality. It does less well in terms of video realism, as the inpainting of the “keyframes” in the first stage is done without conditioning on the known pixels of frames in their immediate vicinity, potentially leading to incompatibility with the local context.

**Table 2: Effect of sampling schemes, measured on the Traffic-Scenes test set.**
| Sampling Scheme | PSNR$\blacktriangle$ | SSIM$\blacktriangle$ | LPIPS$\blacktriangledown$ | PVCS$\blacktriangledown$ | FID$\blacktriangledown$ | VFID$\blacktriangledown$ | $E_{\text{warp}}$$\blacktriangledown$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| AR (Harvey et al., ) | 31.72 | 0.9595 | 0.0392 | 0.2989 | 8.85 | 0.3016 | $3.01{\cdot}10^{\shortminus 4}$ |
| Hierarchy-2 (Harvey et al., ) | 35.53 | 0.9794 | 0.0196 | 0.1732 | 4.55 | 0.1714 | $2.48{\cdot}10^{\shortminus 4}$ |
| Lookahead-AR++ (ours) | 35.29 | 0.9783 | 0.0202 | 0.1725 | 4.87 | 0.1637 | $\mathbf{2.26{\cdot}10^{\shortminus 4}}$ |
| Multires-AR-3 (ours) | 35.32 | 0.9785 | 0.0210 | 0.1815 | 5.07 | 0.1821 | $2.43{\cdot}10^{\shortminus 4}$ |

Samplers and Number of Sampling Steps. The use of diffusion models allows for a trade-off between computation time and inpainting quality by varying the sampler and number of steps used in the generative process. In Table 3 we report the results of our model on the BDD-Inpainting test set with the AR sampling scheme using different samplers and numbers of sampling steps. Aligning with expectations and qualitative observations, performance on metrics degrades as the number of sampling steps decreases for LPIPS, PVCS, FID and VFID. We note, however, that performance on $E_{\text{warp}}$, PSNR, and SSIM improves, suggesting that these metrics may not correlate well with perceived quality, as has been previously suggested for the latter two metrics in Zhang et al. ().

**Table 3: Effect of diffusion samplers, using AR sampling on BDD-Inpainting.**
| Sampler | PSNR$\blacktriangle$ | SSIM$\blacktriangle$ | LPIPS$\blacktriangledown$ | PVCS$\blacktriangledown$ | FID$\blacktriangledown$ | VFID$\blacktriangledown$ | $E_{\text{warp}}$$\blacktriangledown$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Heun (10 steps) | 33.00 | 0.9687 | 0.0339 | 0.2437 | 2.39 | 0.1155 | $\mathbf{1.66{\cdot}10^{\shortminus 3}}$ |
| Heun (25 steps) | 33.05 | 0.9703 | 0.0290 | 0.2202 | 1.84 | 0.0815 | $1.78{\cdot}10^{\shortminus 3}$ |
| Heun (50 steps) | 33.00 | 0.9699 | 0.0282 | 0.2177 | 1.81 | 0.0766 | $1.82{\cdot}10^{\shortminus 3}$ |
| Heun (100 steps) | 32.98 | 0.9700 | 0.0280 | 0.2171 | 1.76 | 0.0788 | $1.85{\cdot}10^{\shortminus 3}$ |
| DDPM (1000 steps) | 32.41 | 0.9665 | 0.0272 | 0.2244 | 1.68 | 0.0779 | $2.39{\cdot}10^{\shortminus 3}$ |

## 6 Conclusion

In this work, we have presented a framework for video inpainting by conditioning video diffusion models. This framework allows for flexible conditioning on the context, enabling post-hoc experimentation with sampling schemes which can greatly improve results. We introduce four challenging tasks for video inpainting methods and demonstrate our model’s ability to use semantic information in the context to solve these tasks effectively. Our experiments demonstrate a clear improvement on quantitative metrics and, contrary to existing methods, our approach can generate semantically meaningful completions based on minimal information in the context.

Acknowledgements. We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada CIFAR AI Chairs Program, Inverted AI, MITACS, the Department of Energy through Lawrence Berkeley National Laboratory, and Google. This research was enabled in part by technical support and computational resources provided by the Digital Research Alliance of Canada Compute Canada (alliancecan.ca), the Advanced Research Computing at the University of British Columbia (arc.ubc.ca), and Amazon.

## Appendix A Limitations

The most notable limitation of our method is its computational cost relative to competing methods. The wall-clock time for our method is highly dependent on a number of factors, such as the number of sampler steps used and the number of frames generated in each stage of the sampling scheme used. Improvements in few-step generation for diffusion models and increasing the size of our model such that more frames can be processed at a time would both help to mitigate these concerns. Additionally, our method requires that a model be trained on a specific dataset which is reasonably close to the data distribution to be inpainted at test time, requiring large datasets. The development of large scale, general purpose video generative models would likely help to make generative approaches such as ours more robust to out-of-distribution tasks.

## Appendix B Potential Negative Impacts

The development of generative models for the generation of photo-realistic video opens the door to malicious or otherwise unethical uses which could have negative impacts on society. While our method is currently limited to operating on data which is sufficiently similar to the datasets it was trained on, and those datasets have limited potential for malicious use, future work which takes a generative approach to video inpainting could be used for the purposes of misinformation, harassment or deception.

## Appendix C Sampling Scheme Details

Figure 8 depicts the sampling schemes used in all experiments for a video length of 200 to supplement the descriptions given in the main text.

Figure: (a) AR.
Refer to caption: extracted/5911903/figures/big-sampling-scheme-visualizations/autoreg.png

## Appendix D Dataset Details

In Section D.1 we detail how our datasets were generated. Section D.2 shows representative examples from each of our datasets.

### D.1 Dataset Creation

#### D.1.1 BDD-Inpainting

From the original BDD100K dataset we take a random subset of videos, using 48,886 for a training set and 100 for each held-out test set. Videos are downsampled spatially by a factor of 2.5, center-cropped to $256\times 256$, and the frame rate is reduced by a factor of three to 10 fps. We truncate videos to 400 frames, corresponding to a 40 second length for each. We randomly generate a set of 49,970 masks of four types: grids, horizontal or vertical lines, boxes, and blobs. Our mask generation procedure first picks a mask type, and then randomly selects mask parameters such as size and direction of motion (which includes stationary masks). Generated masks contain 400 frames. During training both videos and masks are sampled uniformly and independently, giving a distribution over nearly 2.5 billion video-mask pairs. Our test sets, as well as code for regenerating our training set, will be made public upon publication.

#### D.1.2 Inpainting-Cars

We use an in-house dataset of overhead drone footage of vehicle traffic, for which we have tracker-generated bounding boxes for each vehicle. The videos are spatially downsampled by a factor of two from their original 4k resolution, and $256\times 256$ sub-videos centred on vehicles are extracted using the vehicle-specific tracks. The length of these videos is variable as it depends on the amount of time a given vehicle was visible in the source video, but typically ranges from 10 to 60 seconds at 10 fps. Bounding boxes are dilated by a factor of two to ensure the entirety of the vehicle is contained within them, and these dilated bounding boxes are then used as masks. This dataset contains 2973 training examples and 100 held-out test examples.

#### D.1.3 Traffic Scenes

This dataset is created using the same in-house dataset as was used for Inpainting-Cars. The 4k, 10 fps source videos are spatially downsampled by a factor of 7.25, truncated to 200 frames and cropped to $256\times 256$, with the crops centred over road features like intersections, roundabouts, highway on-ramps, *etc*. We generate masks using the same mask generation procedure as in BDD-Inpainting.

### D.2 Dataset Examples

#### D.2.1 BDD-Inpainting

See Fig. 9 for examples from the BDD-Inpainting dataset. Mask types, shown in green, are indicated for each example.

Figure: Figure 9: Representative examples from the BDD-Inpainting dataset.
Refer to caption: x6.png

#### D.2.2 Inpainting-Cars

See Fig. 10 for examples from the Inpainting-Cars dataset. Masks are shown in black.

Figure: Figure 10: Representative examples from the Inpainting-Cars dataset.
Refer to caption: x7.png

#### D.2.3 Traffic-Scenes

See Fig. 11 for examples from the BDD-Inpainting dataset. Mask types, shown in green, are indicated for each example.

Figure: Figure 11: Representative examples from the Traffic-Scenes dataset.
Refer to caption: extracted/5911903/figures/dataset-examples/ts-examples.png

## Appendix E Training Details

All models are trained with 4 $\times$ NVIDIA A100 GPUS with a batch size of one, corresponding to an effective batch size of 4 as gradients are aggregated across GPUs.
All models were trained to condition on or generate 16 frames at a time, use an EMA rate of 0.999, and use the AdamW Loshchilov & Hutter () optimizer. We use the noise schedules defined in Chen Chen (): cosine for Inpainting-Cars, and sigmoid for BDD-Inpainting and Traffic-Scenes. All models use temporal attention at the spatial resolutions (32, 16, 8) in the U-Net, except for the Inpainting-Cars model where temporal attention only occurs at resolutions (16, 8). Otherwise, the default hyperparameters from our training code are used for all models. This code, and the BDD-Inpainting checkpoint, will be released upon publication. The number of training iterations for each model is listed below:

| Dataset | Iterations (millions) |
| --- | --- |
| BDD-Inpainting | 2.5 |
| Inpainting-Cars | 1.1 |
| Traffic-Scenes | 2.4 |

## Appendix F Additional Ablations

### F.1 Additional Sampling Scheme Ablations

Table 4 and Table 5 show the effect of using different sampling schemes on the BDD-Inpainting and BDD-Inpainting-Blobs test sets, respectively. The sampling schemes tested are depicted in Fig. 3 and Fig. 5. For each metric, the best performing sampling scheme is indicated in bold font. All measurements were taken using the Heun sampler Karras et al. () with 100 sampling steps. The Lookahead-AR++ performs best on both test sets across almost all metrics. Sampling scheme ablations are omitted for Inpainting-Cars as we found an autoregressive scheme gave significantly better qualitative results than the other schemes.

**Table 4: Effect of sampling schemes measured on the BDD-Inpainting test set.**
| Sampling Scheme | PSNR$\blacktriangle$ | SSIM$\blacktriangle$ | LPIPS$\blacktriangledown$ | PVCS$\blacktriangledown$ | FID$\blacktriangledown$ | VFID$\blacktriangledown$ | $E_{\text{warp}}$$\blacktriangledown$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Multires-AR-3 | 32.81 | 0.9678 | 0.0289 | 0.2302 | 1.75 | 0.0884 | $2.19{\cdot}10^{\shortminus 3}$ |
| Lookahead-AR++ | 33.68 | 0.9717 | 0.0261 | 0.2037 | 1.71 | 0.0748 | $\mathbf{1.79{\cdot}10^{\shortminus 3}}$ |
| AR | 32.97 | 0.9699 | 0.0278 | 0.2166 | 1.78 | 0.0778 | $1.85{\cdot}10^{\shortminus 3}$ |
| Hierarchy-2 | 32.96 | 0.9690 | 0.0284 | 0.2232 | 1.74 | 0.0839 | $1.97{\cdot}10^{\shortminus 3}$ |
| Multires-AR-2 | 33.18 | 0.9692 | 0.0278 | 0.2201 | 1.72 | 0.0815 | $2.03{\cdot}10^{\shortminus 3}$ |
| Reverse AR | 33.31 | 0.9702 | 0.0273 | 0.2132 | 1.76 | 0.0785 | $\mathbf{1.79{\cdot}10^{\shortminus 3}}$ |

**Table 5: Effect of sampling schemes measured on the BDD-Inpainting-Blobs test set.**
| Sampling Scheme | PSNR$\blacktriangle$ | SSIM$\blacktriangle$ | LPIPS$\blacktriangledown$ | PVCS$\blacktriangledown$ | FID$\blacktriangledown$ | VFID$\blacktriangledown$ | $E_{\text{warp}}$$\blacktriangledown$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Multires-AR-3 | 29.89 | 0.9561 | 0.0475 | 0.3142 | 1.63 | 0.1188 | $2.00{\cdot}10^{\shortminus 3}$ |
| Lookahead-AR++ | 30.67 | 0.9608 | 0.0442 | 0.2857 | 1.69 | 0.1083 | $1.53{\cdot}10^{\shortminus 3}$ |
| AR | 29.45 | 0.9547 | 0.0512 | 0.3319 | 2.07 | 0.1328 | $1.61{\cdot}10^{\shortminus 3}$ |
| Hierarchy-2 | 29.97 | 0.9570 | 0.0474 | 0.3106 | 1.65 | 0.1137 | $1.74{\cdot}10^{\shortminus 3}$ |
| Multires-AR-2 | 30.25 | 0.9583 | 0.0454 | 0.2991 | 1.59 | 0.1116 | $1.79{\cdot}10^{\shortminus 3}$ |
| Reverse AR | 30.04 | 0.9590 | 0.0450 | 0.2920 | 1.8 | 0.1134 | $\mathbf{1.51{\cdot}10^{\shortminus 3}}$ |

### F.2 Qualitative Sampling Scheme Ablations

In this section we highlight the qualitative impact that different sampling schemes can have on the quality of inpainted videos. We select a video from the test set where conditioning on the appropriate frames is crucial for our method to succeed. Figure 13 shows the beginning of this video, where a car is occluded in the right-hand lane for the first few seconds of the video (see input frames in the first row). The Autoregressive scheme (second row) shows a distinct “pop-in” effect, as when the initial frames were generated the model was not able to condition on future frames where the cars existence and appearance are revealed. Both Reverse-Autoregressive and AR w/ Far Future (third and fourth rows) do condition on future frames that contain the car; Reverse-Autoregressive because the model is able to propagate the car backwards through time, and AR w/ Far Future because the model conditions on frames far out into the future where the car has been revealed. Figure 13 shows the end of the video, where the cars in the right-hand lane are occluded and remain occluded for the rest of the video (first row). The Autoregressive scheme keeps these cars visible, as it is able to propagate them forward through time (second row). Reverse-Autoreg (third row) fails for the same reason that Autoreg did at the beginning: when the final frames were generated, the model was not able to condition on frames where the vehicles were visible. AR w/ Far Future (fourth row) is again successful; despite the cars not becoming visble again (and thus there is no “future” to condition on), it is able to propagate the cars forward in time as the Autoreg scheme does.

Figure: Figure 12: Qualitative results from different sampling schemes on the beginning of the video discussed in Section F.2
Refer to caption: x8.png

## Appendix G Diffusion Model Sampling Details

As alluded to in the main paper, we train a diffusion model that uses $\epsilon$-prediction to parameterize the score of a distribution over $\mathbf{x}_{t}$ at each time $t$. This can be used to parameterize a stochastic differential equation (or ordinary differential equation) that morphs samples from a unit Gaussian into approximate samples from the conditional distribution of interest $p_{\text{data}}(\mathbf{x}|\mathbf{y})$ Song et al. (). We use the Heun sampler proposed by Karras et al. () to integrate this SDE. Our hyperparameters are $\sigma_{\text{max}}=1000$, $\sigma_{\text{min}}=0.002$, $\rho=7$, $S_{\text{churn}}=80$, $S_{\text{max}}=\infty$, $S_{0}=0$, and $S_{\text{noise}}=1$. We use 100 sampling steps (involving 199 network function evaluations) for each experiment except where specified otherwise.

## Appendix H Additional Qualitative Results

### H.1 BDD-Inpainting

Figure 15 and Figure 15 show additional results on tasks from the BDD-Inpainting test set.

Figure: Figure 14: Qualitative results from our method and all the competing methods we compared to quantitatively on an example from the BDD-Inpainting test set. We note that, in the presence of small masks, the qualitative differences are less pronounced between our method and the best-performing benchmarks, like since information in neighbouring frames can more easily be exploited. For our method we use the Lookahead-AR++ sampling scheme.
Refer to caption: x10.png

Figure: Figure 15: Qualitative results from our method and all the competing methods we compared to quantitatively on an example from the BDD-Inpainting test set. Again, for our method we use the Lookahead-AR++ sampling scheme. In this video the vehicle passing in the left-hand lane is only ever partially visible, causing other methods to produce blurry results.
Refer to caption: x11.png

### H.2 Traffic-Scenes

Figure 16 and Figure 17 show additional results on tasks from the Traffic-Scenes training set.

Figure: Figure 16: Qualitative results from our method and all the competing methods we compared to quantitatively on an example from the Traffic-Scenes test set. For our method we use the Hierarchy-2 sampling scheme. For our method the two occluded vehicles are inpainted with continuous trajectories; for all other methods the vehicles disappear while they are occluded.
Refer to caption: x12.png

Figure: Figure 17: Further qualitative results from our method and all the competing methods we compared to quantitatively on an example from the Traffic-Scenes test set. In the ground truth for this example, in the first frame there is a vehicle making a left hand turn towards the bottom of the image that is occluded until it briefly emerges many frames later. Our model is able to initialize a vehicle in the inpainting and complete a trajectory which is consistent with the exit point for the vehicle. In all other methods the vehicle appears suddenly.
Refer to caption: x13.png

### H.3 Inpainting-Cars

Figure 18 shows additional results on tasks from the Traffic-Scenes training set.

Figure: (a)
Refer to caption: x14.png

## Peer Reviews

confidence: 2
contribution: 2
initial_rating: 6
presentation: 3
questions: Please see the weaknesses.
soundness: 3
strengths: 1. The method effectively captures temporal and spatial dependencies, enabling high-quality synthesis for occluded scenes and objects.
2. Quantitative results show clear improvements over state-of-the-art methods, especially in challenging video inpainting scenarios.
summary: This paper presents a conditional generative framework for video inpainting that leverages diffusion models to synthesize new, semantically coherent content for large occlusions. Unlike traditional methods dependent on optical flow, this approach achieves realistic object behavior and maintains temporal consistency even when entire content regions must be generated. Evaluated on newly constructed datasets, the framework demonstrates superior quality and consistency in inpainted sequences compared to existing methods.
weaknesses: 1. Please provide inference costs (e.g., time and peak GPU memory usage) for a specified input size and number of frames, and compare these with other SOTA methods.

2. A dedicated ablation study comparing the method trained on standard datasets used by other methods versus the newly introduced datasets is suggested. This would help isolate the effects of the method itself from those of the datasets, as data alone can often address significant aspects of the problem.

3. The framework lacks clear illustration, making it difficult to grasp the main concepts. For instance, it is unclear how conditional inputs are processed in the forward pass, the roles of the trainable and frozen modules, or if any new layers for controllable generation are introduced.

4. Please also discuss the potential limitations or failure cases of the proposed method. Beyond inpainting cars, can this method inpaint other objects in a zero-shot manner?

---

confidence: 3
contribution: 3
initial_rating: 8
presentation: 3
questions: Is the model trained from scratch? How well does it generalize to other, more complicated open video domains?
soundness: 4
strengths: - The paper is easy to follow
- The idea is intuitive and works also practically well
- The authors showcased that their model is better than flow- and attention-based video inpainting methods both qualitatively and quantitatively
- Different sampling schemes are also evaluated extensively.
summary: The authors extend the idea of Flexible Diffusion Model (FDM) to the video inpainting domain. A different sampling and training scheme is also developed to accomodate the new task.
weaknesses: Given that the authors proposed a variety of inference techniques, it would be interesting to investigate whether different sampling schemes during **training** makes a difference. It seems from Equation 3 that the $\mathcal{X}$ and $\mathcal{Y}$ are randomly sampled; would a benefit be gained e.g., when training the model in a similar way to how you sample at inference?

---

confidence: 5
contribution: 2
initial_rating: 5
presentation: 2
questions: Inference time: How long does the proposed model take to process a typical 2-second video clip?
soundness: 3
strengths: 1. The authors invest significant effort in building a large-scale video dataset to train the diffusion model.
2. Compared to previous video inpainting methods, the proposed model emphasizes filling in unseen content rather than relying solely on propagation.
summary: State-of-the-art video inpainting methods focus on propagating visual information but struggle with generating novel content. This paper redefines video inpainting as a conditional generative task using video diffusion models, enabling synthesis of inpaintings with long-range temporal consistency. The approach leverages inpainting-specific sampling schemes to handle incomplete frames and is effective even when objects are partially visible or absent in the initial context.
weaknesses: 1. Practicality concerns: The proposed dataset is heavily focused on scenarios involving cars, which restricts its generalizability to broader real-world applications.

2. Lack of control over inpainting content: The proposed model does not offer mechanisms to specify what should fill the missing regions.

3. Limited diversity in demonstrations: All examples shown in the paper involve cars in the inpainted regions. Are there demonstrations with objects other than cars?

4. Comparison with recent methods: Some recent mask-guided video editing techniques can also fill gaps with cars, offering options to specify the car model and color via text prompts. Why is there no comparison with these methods?

5. The claim of “strong experimental results” is potentially biased. The authors only test their model on BDD-Inpainting and Traffic-Scenes (datasets used for model training), while baselines are only trained on Youtube-VOS and DAVIS. This discrepancy in training data limits fair comparison.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: - Could you also provide quantitative comparisons of your method on the YouTube-VOS and DAVIS datasets? Additional results for object removal tasks would be more convincing.
soundness: 3
strengths: - The motivation is clear and the techniques sound reasonable.
- The writing and organization are easy to follow.
- The experiment result validates the effectiveness of this approach, especially for complex scenarios where occlusions (input information is scarce) prevent conventional methods from performing well.
summary: This paper introduces a novel video inpainting framework based on conditional diffusion models, which can generate semantically consistent content that remains realistic over extended occlusions and diverse scene complexities. The authors present sampling schemes tailored for inpainting that capture essential long-range dependencies within the context and develop a new method to condition on known pixels in incomplete frames. Results show that the proposed method consistently outperforms methods based on optical flow or attention.
weaknesses: 1. The author needs to highlight this paper’s innovative contributions in the INTRODUCTION part and present a completed method graph rather than just showing model inputs.
2. This work relies heavily on the inpainting model to handle object occlusions and interactions. However, the authors do not address how the model performs in highly cluttered or dense environments with multiple possible object placements.
3. Additional experiments are needed to demonstrate the effectiveness of the sampling schemes and the model's generalization to non-traffic datasets, along with further comparisons/discussions with recent works, such as [1] [FFF-VDI](https://arxiv.org/pdf/2408.11402) [2] [FGT++](https://arxiv.org/pdf/2301.10048) [3] [SViT](https://openaccess.thecvf.com/content/ICCV2023/papers/Lee_Semantic-Aware_Dynamic_Parameter_for_Video_Inpainting_Transformer_ICCV_2023_paper.pdf).

Meta-review: Summary: This paper introduces a conditional diffusion model for video inpainting over a long temporal context. Unlike traditional approaches, it avoids reliance on optical flow and presents an inpainting-specific sampling scheme.
Strengths: The proposed approach is intuitive and clearly motivated. It demonstrates advantages over flow- and attention-based methods, as shown through both qualitative and quantitative evaluations. 
Weaknesses: As noted by three reviewers, the paper lacks key experimental results and ablations. In particular, there is no ablation study comparing performance on standard datasets versus the newly introduced ones, which would help isolate the method’s contributions from the dataset’s impact. Furthermore, the methodology’s scope is primarily focused on traffic-related scenes, limiting its broader applicability. Reviewer i15w also highlighted the absence of ablations on the sampling scheme's generalization to non-traffic datasets, an issue that remains unaddressed by the authors. The rebuttal has not sufficiently alleviated these concerns to justify overriding the reviewers’ assessments.

Decision: Reject