Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model

Authors: min  zhao
hongzhou  zhu
chendong  xiang
kaiwen  zheng
chongxuan  li
jun  zhu
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-14 17:26:48

Abstract: Diffusion models have obtained substantial progress in image-to-video generation. However, in this paper, we find that these models tend to generate videos with less motion than expected. We attribute this to the issue called conditional image leakage, where the image-to-video diffusion models (I2V-DMs) tend to over-rely on the conditional image at large time steps. We further address this challenge from both inference and training aspects. First, we propose to start the generation process from an earlier time step to avoid the unreliable large-time steps of I2V-DMs, as well as an initial noise distribution with optimal analytic expressions (Analytic-Init) by minimizing the KL divergence between it and the actual marginal distribution to bridge the training-inference gap. Second, we design a time-dependent noise distribution (TimeNoise) for the conditional image during training, applying higher noise levels at larger time steps to disrupt it and reduce the model's dependency on it. We validate these general strategies on various I2V-DMs on our collected open-domain image benchmark and the UCF101 dataset. Extensive results show that our methods outperform baselines by producing higher motion scores with lower errors while maintaining image alignment and temporal consistency, thereby yielding superior overall performance and enabling more accurate motion control. The project page: \url{https://cond-image-leak.github.io/}.

Main paper: 
 introduction: 
 Image-to-video (I2V) generation aims to generate videos with dynamic and natural motion while maintaining the content of the given image. It allows users to guide video creation from the input image (and optional text), thus increasing controllability and flexibility in content creation. Like the remarkable progress in text-to-image (T2I) generation [42, 39, 21, 8, 18, 5 ] and text-to-video (T2V) generation [10, 46, 6, 11] , diffusion models have also obtained promising results for I2V generation [9, 15, 63, 12, 69, 70] . However, such models are not fully understood.In this paper, we observe that existing image-to-video diffusion models (I2V-DMs) tend to generate videos with less motion than expected (see Fig. 1 ). We attribute this to a previously overlooked issue called conditional image leakage (see Sec. 3.1) . Normally, the noisy input contains the motion information of the target video, and I2V-DMs should rely on it to predict motion, while the static conditional image provides content guidance. However, in practice, as the diffusion process The issue of existing I2V-DMs. Regardless of input motion scores (Input MS), the output motion scores (Output MS) are consistently lower than expected. In contrast, our method yields output motion scores either higher or lower than Input MS with reduced error.progresses-especially at large time steps, the noisy input becomes heavily corrupted, while the conditional image preserves extensive detail of the target video. This biases the model to over-rely on the conditional image and neglect the noisy input, leading to videos with reduced motion. To validate this, we corrupt the ground truth (GT) clean video and compute one-step clean video predictions at each time step. As shown in Fig. 2 , the predicted clean videos exhibit markedly reduced motion than GT at large time steps, indicating leakage.Based on the above analysis, we attempt to address the challenge from both inference and training aspects (see Sec. 3.2) . First, we present a simple yet effective inference strategy that starts the video generation process from an earlier time step, thus avoiding the unreliable large-time steps of the I2V-DMs. To further enhance performance, we derive an initial noise distribution with optimal analytic expressions (Analytic-Init) by minimizing the KL divergence between it and the true marginal distribution to bridge the training-inference gap. Second, to mitigate leakage during training, we propose a time-dependent noise distribution (TimeNoise) that increases noise levels at larger time steps, effectively disrupting the conditional image and reducing model dependency on it. This is achieved by employing a logit-normal distribution with a center that gradually shifts over time. Finally, our method achieves higher motion scores with reduced motion score error and ensures that the predicted clean video maintains motion dynamics comparable to the ground truth across all time steps, effectively mitigating conditional image leakage. Notably, our general strategies are adaptable to various I2V-DMs based on both VP-SDE [12, 63, 70] and VE-SDE [9] framework.Empirically, we validate our methods on various I2V-DMs [9, 63, 12, 70] using our collected opendomain images (ImageBench) and the UCF101 dataset. We conduct a user study on ImageBench and report FVD [55] , IS [7] , and motion score [9] on UCF101. For motion-conditioned models [9, 70] , we also report the motion score error between the generated video and the input motion score at different levels. Extensive experimental results demonstrate that our strategies outperform baselines by producing higher motion scores with lower errors while maintaining image alignment and temporal consistency, thereby yielding superior overall performance and enabling more accurate motion control.2 Background Diffusion Models. Diffusion models gradually perturb the data x 0 ∼ q(x 0 ) via a forward diffusion process and reverse the process to recover it. The forward transitional kernel q t|0 (x t |x 0 ) is given byEQUATIONwhere α t and σ t are the noise schedule chosen to ensure that x T contains minimal information about x 0 . Such forward diffusion processes can be viewed as stochastic differential equations (SDEs), among which two prevalent types are commonly used [48, 29] . One is the variance-preserving SDE (VP-SDE) [42, 24] , where α 2 t + σ 2 t = 1 with α t → 0 as t → T , ensuring p T (x T ) = N (x T ; 0, I). The other is the variance exploding SDE (VE-SDE), where α t ≡ 1 and σ T is set to a large constant, resulting in p T (x T ) ≈ N (x T ; 0, σ 2 T I). Such models can be parameterized with a noise-prediction model ϵ θ (x t , t) (ϵ-prediction) [24] ,and the parameters are learned by minimizing: where the noisy input x t ∼ q t|0 (x t |x 0 ). Alternative parametrizations such as x 0 -prediction [29] , v-prediction [45] , F -prediction [28] are also commonly applied. The ϵ-prediction and x 0 -prediction aims to predict the added noise or clean video from noisy input x t . Starting from x T ∼ p T (x T ), various samplers [47, 33, 34, 28] can be employed to generate data. More recent studies [66] further demonstrate that diffusion models can generate realistic images with controllable semantics given only a few labels.EQUATIONDiffusion Models for Image-to-Video Generation. Given an image y 0 from the open domain, the goal of I2V is to generate a video X 0 = {x i 0 } N i=1 with dynamic and natural motion while keeping alignment with the appearance of y 0 . This task can be formulated as designing a conditional distribution p θ (X 0 |y 0 ), which is achieved by a conditional diffusion model minimizing:EQUATIONwhere X t ∼ q t|0 (X t |X 0 ). Typically, y 0 is the first frame of X 0 and DynamiCrafter [63] adopts a randomly selected frame from X 0 as y 0 . The key issue is to effectively integrate the conditional image y 0 into the diffusion model. Most methods use CLIP image embeddings [40] to maintain the semantic content of y 0 . Notably, VideoCrafter1 [12] and Dynamicrafter [63] employ the last layer's full patch visual tokens from the CLIP ViT, enriching the encoded information, and other approaches prefer the class token layer. Yet, solely depending on these embeddings, such as in VideoCrafter1 [12] , compromises detail retention, resulting in degradation of the image alignment. To enhance detail representation, I2VGen-XL [69] combines the conditional image with the noisy initial frame, while VideoComposer [57] develops a STC-encoder for multiple conditions. Although superior to CLIP image embeddings, these strategies still fail to fully retain the conditional image content. To mitigate this, AnimateAnything [15] , Dynamicrafter [63] and SVD [9] directly concatenate noisy video X t with y 0 , which injects detailed information to the model. Apart from the prior work mentioned before, we discuss other related work about diffusion models for image generation and video generation in the Appendix E. 
method: 
 Although existing I2V-DMs discussed in Sec. 2 have achieved significant progress, such models are not fully understood. In this section, we first identify a critical yet previously overlooked issue in I2V-DMs: conditional image leakage (CIL) (see Sec. As shown in Fig. 1 , we observe that, regardless of the input motion scores, the motion scores of generated videos from existing I2V-DMs [9, 70] are consistently lower than expected. This raises the question: why do these models always produce lower motion scores, rather than fluctuating above or below the expected values, as observed in our method?To understand this, we need to consider the source of motion information in the generated videos, which comes from the noisy input X t . Ideally, I2V-DMs should rely primarily on X t for motion, with the static conditional image y 0 providing content guidance. However, as shown in Fig. 2 , at large time steps, the noisy input X t becomes increasingly corrupted, while the conditional image y 0 retains significant information of the target video. This biases the model to over-rely on the conditional image and neglect the noisy inputs, leading to videos with reduced motion.To validate this, we corrupt a ground truth (GT) clean video X 0 via the forward transition kernel in Eq. ( 1) and use it as the noisy input to compute the one-step prediction Xt→0 at time t:EQUATIONIdeally, Xt→0 should predict the GT X 0 from noisy input X t and exhibit comparable motion dynamics. However, as shown in Fig. 2 , as time progresses-particularly at large time steps, Xt→0 exhibits markedly reduced motion than GT, indicating the conditional image leakage. This results in videos with reduced motion starting from time T . Notably, recent techniques that adjust the noise schedule towards higher noise levels [14, 27, 9, 32] may further exacerbate this issue (see Appendix C for details).Building upon the above analysis, this section presents general strategies to address the issue of conditional image leakage in both the inference and training aspects. Inference strategy. As discussed in Sec. 3.1, conditional image leakage easily occurs at large time steps. To this end, a straightforward solution is to start the generation process from an earlier time step M ∈ (0, T ), thus avoiding the unreliable later stages of I2V-DMs. Let p M (X M ) denote the initial noise distribution at the start time M . Initially, we set p M (X M ) = p T (X T ), i.e. N (0, I) in VP-SDE [24, 63, 12, 70] or N (0, σ 2 T I) in VE-SDE [29, 9] . As illustrated in Fig. 3 (a), this straightforward strategy markedly improves motion dynamics without sacrificing other performance. However, a smaller M value (e.g., M = 0.8T ) results in poor visual quality due to the traininginference discrepancy.To mitigate this gap, we propose Analytic Noise Initialization (Analytic-Init) to refine the initial noise distribution p M (X M ) by minimizing the KL divergence between it and the true marginal distribution q M (X M ) of the forward diffusion process. Inspired by previous work [4, 3, 64] , we demonstrate that when p M (X M ) is modeled as a normal distribution N (X M ; µ p , σ 2 p I), the optimal mean µ * p and variance σ 2 * p have analytical solutions, as stated in Proposition 1. Proposition 1. Given a normal distribution p M (X M ) = N (X M ; µ p , σ 2 p I) and q M (X M ) is the margin distribution of diffusion forward process at time M , with the forward trainsition kernelq M |0 (X M |X 0 ) = N (X M ; α M X 0 , σ 2 M I), the minimization problem min µp,σ 2 p D KL (q M (X M )||p M (X M)) yields the following optimal solution: where q(X 0 ) denotes the data distribution , d denotes the dimension of the data, and X (j) 0 denotes the j-th component of X 0 .EQUATIONThe proof of Proposition 1 is provided in Appendix A. Empirically, µ * p and σ 2 * p can be estimated using the method of moments [4] . The steps for inference are outlined in Algorithm 1. As demonstrated by the qualitative results in Fig. 3 (a) and quantitative results in Tab. 8, Analytic-Init improves video quality by reducing the training-inference gap, especially for smaller M . Finally, as shown in Fig. 3 (b) and Tab. 1, Analytic-Init produces higher motion scores with lower errors, allowing for more accurate motion control and reducing conditional image leakage.Training strategy. In this section, we show how to address the issue of conditional image leakage during the training phase. As outlined in Sec. 3.1, the conditional image y 0 retains substantial details of the target video, causing I2V-DMs to rely heavily on it. To mitigate this, a natural approach is to perturb y 0 to relieve this dependency. Our first attempt is to introduce noise at a similar scale to that in X t , aiming to balance the model's challenge of predicting clean video from X t or y 0 , thereby lessening reliance on y 0 . However, this strategy also makes it difficult to employ y 0 , resulting in lower video quality.To overcome this, we propose a noise distribution on y 0 that introduces substantial noise to prevent leakage while maintaining a cleaner y 0 to aid content generation. Given that X t contains less information about X 0 as time progresses, increasing the risk of leakage, we further develop a timedependent noise distribution p t (β s ) (TimeNoise). The key principle is to favor high noise levels at large time steps to sufficiently disrupt y 0 , shifting towards lower noise levels as the time step decreases. To achieve this, we employ a logit-normal distribution [18, 1] defined as below:EQUATIONwhere logit( βs βm ) follows a normal distribution centered around µ(t) with a standard deviation of 1. This noise distribution includes two hyperparameters: β m , the maximum noise level, and µ(t), the center of the distribution. As illustrated in Fig. 4 (a), we can adjust µ(t) over time t to satisfy the previously mentioned design principle. Finally, the noisy conditional image y s at time t is obtained by y s = y 0 + β s ϵ, where β s ∼ p t (β s ), ϵ ∼ N (0, I). We also tried other adding noise choices but found them to be less effective (see Appendix D). During inference, we add a fixed noise level to the conditional image across all time steps, following CDM [25] , as the model is trained with varying noise levels. Empirically, we find directly using the clean conditional image performs well and thus adopt it for simplicity. The full algorithm is shown in Algorithm 2. I2VGen-XL [69] and VideoCrafter1 [12] mitigates the leakage at the expense of image alignment. The SVD produces videos with camera movements while keeping objects relatively static to meet high motion scores, while ours generates videos that feature both natural and dynamic object movements.Next, we conduct a systematic analysis of the two hyperparameters to investigate their impact on video generation. Firstly, µ(t) is designed to increase monotonically with time, ranging from µ(0) = -1 to µ(T ) = 1. To formalize this, we define µ(t) as a power function: µ(t) = 2t a -1, where a > 0. This formulation allows flexible tuning of a to control the monotonic behavior, where smaller values of a cause higher noise levels to be sampled at later time steps. As shown in Fig. 4 (b), (1) a = 1 corresponds to a linear increase; (2) a ∈ (0, 1) represents a concave function, indicating a faster noise level increase; and (3) a > 1 corresponds to a convex function, indicating a slower increase in noise levels over time. As shown in Fig. 4 (c), higher noise levels (e.g., when a < 1) lead to increased dynamic motion but reduced temporal consistency and image alignment. For the maximum noise level β m , the only constraint is that it must be greater than 0. As shown in Fig. 4 (c), a higher β m enhances dynamic motion but decreases temporal consistency and image alignment, while a lower β m reduces motion. Additionally, we apply TimeNoise to the CLIP Image Embedding for both VideoCrafter1 [12] and DynamiCrafter [63] , as they use full patch visual tokens from CLIP, which contain substantial information about the conditional image, increasing the likelihood of leakage.Finally, we replicate the experiments described in Sec. 3.1, with results presented in Fig. 5 and Tab.1. These results demonstrate that TimeNoise achieves higher motion scores with reduced error and ensures that Xt→0 maintains motion dynamics comparable to the ground truth across all time steps, effectively mitigating conditional image leakage.In this section, we analyze popular I2V-DMs [63, 9, 70, 12, 69] through the lens of CIL. Although these models do not explicitly address this issue, we believe their strategies mitigate it to some degree.Firstly, some methods only use partial information from the conditional image, which can help reduce leakage. For example, VideoCrafter1 [12] only utilizes CLIP Image Embedding, and I2VGEN-XL [69] adds y 0 to the first frame of the noisy input. However, as shown in Fig. 6 , the videos generated by these methods do not fully capture the details of y 0 . To address this, models like Dynamicrafter [63] directly incorporate y 0 into I2V-DMs, improving detail preservation but also increasing the risk of leakage. Dynamicrafter selectively refines spatial layers while preserving the pre-trained temporal layers, which contain motion priors and thus maintain motion dynamics to a certain extent. However, it does not inherently solve the leakage issue. Moreover, some methods introduce external signals [9, 70, 65, 62, 60] , forcing the model to align with additional conditions, which reduces its dependence on y 0 and helps mitigate leakage. However, we argue that they do not address the core challenge of I2V-DMs, which should predict clean video primarily from noisy input to capture motion information. As illustrated in Fig. 6 , the SVD often results in static objects with excessive camera movements to meet high motion score requirements. In contrast, our method generates videos with natural, vivid object movements. In summary, while the above methods mitigate VC.A cartoon girl with brown curly hair splashes joyfully in a bubble-filled bathtub.Baseline + Analytic-Init DC.An orange being cut in half and splashing into a liquid, likely milk, swinging fast.A sparkler being lit on the ground.DC.A cat lying on the bed.Conditional image VC. and DC. denote VideoCrafter1 [12] and DynamiCrafter [63] respectively.CIL to some extent, our approach provides a more effective solution to the fundamental challenges in I2V-DMs, enabling more precise motion control and enhancing naturalness by focusing more on the noisy input. 
setup: 
 Datasets. We use WebVid-2M [2] as the training dataset, with all videos resized and center-cropped to 320 × 512 at 16 frames and 3 fps. For evaluation, we use UCF101 [49] and our ImageBench dataset, which includes diverse categories (e.g., nature, humans, animals, plants, food, vehicles) and complex elements like numerals, colors, and intricate scenes, similar to DrawBench [44] . In total, 100 images are collected from various websites and T2I models such as SDXL [39] and UniDiffuser [5] .Evaluation Metrics. On UCF101, we report Fréchet Video Distance (FVD), Inception Score (IS), and Motion Score (MS). For ImageBench, we conduct user studies with 10 subjects to perform pairwise comparisons of our methods against baselines, evaluating motion, temporal consistency, image alignment, and overall performance. For motion-conditioned methods, we also report the motion score error between the generated video and the input motion score at various levels. Motion scores are computed using flow maps following SVD [9] , except for the PIA [70] , where we follow its original algorithm and compute the L1 distance. More details on the metric computations are provided in Appendix B.Implementation Details. For Analytic-Init, we use 5000 samples from the Webvid-2M dataset to estimate p M (X M ) by default. We set M = 0.96T, 0.96T and σ M = 100 for VideoCrafter1 [12] , DynamiCrafter [63] , and SVD [9] on UCF101. For ImageBench, we adjust M to 0.92T, 0.92T and σ M = 100. For the TimeNoise, we set β m = 25, 100, and 100 for VideoCrafter1, DynamiCrafter, and SVD, respectively. The function µ(t) = 2t 5 -1 is applied across all baselines. These baselines are fine-tuned for 20,000 iterations, using either the official code or replication of the official settings except for batch size. More detailed information can be found in the Appendix B. The effectiveness of our inference and training strategy. We validate our strategies on following I2V-DMs: VideoCrafter1 [12] , DynamiCrafter [63] and SVD [54] . We validate our inference strategy against the original method and a naively finetuned version, and our training strategy against a naively finetuned version. The quantitative comparisons and qualitative results are presented in Tab. 1, Fig. 3 , Fig. 5 and Fig. 7 , leading to several key observations. First, our strategies significantly improve motion scores and reduce motion score error, demonstrating their effectiveness in precisely controlling motion degrees and mitigating conditional image leakage. Second, we achieve both dynamic motion and high video quality, as reflected by improved FVD and IS scores. The user study in Fig. 8 further supports that our strategies enhance video dynamism while preserving image alignment and temporal consistency, yielding superior overall performance.Evaluating the combined inference and training strategies. In this section, we aim to investigate the necessity of combining two strategies. As illustrated in Tab. 7, for DynamiCrafter [63] and SVD [9] , our TimeNoise effectively mitigates conditional image leakage, rendering the additional inference strategy less impactful. Conversely, for VideoCrafter1 [12] , which relies solely on CLIP image embedding for information, employing excessive TimeNoise disrupts image alignment. Hence, we utilize a moderate TimeNoise (β m = 25), where the inference strategy remains effective.Comparison with SOTA noise initialization and conditioning augmentation methods. We compare our Analytic-Init with SOTA noise initialization methods [41, 61, 19 ] and our TimeNoise with SOTA conditioning augmentation [25] . As shown in Tab. 2, Analytic-Init outperforms all baselines. It achieves higher motion scores than FrameInit [41] , which limits motion by duplicating the conditional image as static guidance. Compared to FreeInit [61] , our method reduces inference time by more than half while slightly improving performance, as FreeInit's iterative process increases time. Compared to Progressive Noise [19] , Analytic-Init performs better by generating videos from an earlier time step, a gap the baseline cannot handle effectively. As shown in Fig. 9 in the Appendix, CDM [25] results in lower motion, whereas our method produces more dynamic videos. Additionally, a naive baseline using a single value forEQUATIONleads to poor image alignment, further demonstrating the effectiveness of our time-dependent noise distribution. In this paper, we identify a common issue in I2V-DMs: conditional image leakage. We address this challenge from two aspects. First, we introduce a training-free inference strategy that starts the generation process from an earlier time step to avoid the unreliable late-time steps of I2V-DMs. Second, we design a time-dependent noise distribution for the conditional image to mitigate conditional image leakage during training. We validate the effectiveness of these strategies across various I2V-DMs.Limitations and broader impact. One limitation of this paper is the need to balance timedependent noise distribution to prevent conditional image leakage while maintaining image integrity.While we demonstrate the effectiveness of our training strategy on existing image-to-video diffusion models, we do not provide a definitive noise distribution choice for a scratch-trained model. We leave this in the future work. Furthermore, we must use the method responsibly to prevent any negative social impacts, such as the creation of misleading fake videos.According to Lemma 2 of Analytic-DPM [4] , the KL divergence between initial noise distribution p M (X M ) and the actual marginal distribution q M (X M ) can be expressed as:EQUATIONwhere µ q , Σ q denote expectation and covariance matrix of q M (X M ), and H(•) denotes the entropy of a distribution. Since q M (X M ) is determined by the forward diffusion process and is independent of the µ p and σ 2 p in p M (X M ), the last two terms of Eq. ( 7) can be considered as constants. Therefore, the optimization objective can be formulated as:EQUATION)According to the property of the KL divergence between two normal distributions, D KL (N (X M ; µ q , Σ q )||N (X M ; µ p , σ 2 p I)) could be expanded as:EQUATIONwhere d denotes the dimension of the data. Minimizing Eq. ( 9) yields:EQUATION)Taking the derivative of Eq. 9 with respect to σ 2 p , the optimal σ 2 p comes to:EQUATIONEQUATIONEQUATION)We now represent µ * p and σ 2 * p with X 0 . Taking the X M defined in Eq. ( 1) into Eq. ( 10), the optimal µ * p could be further represented asEQUATIONEQUATIONSimilarly, for the optimal variance σ 2 * p , we can deriveEQUATIONGiven that X 0 and ϵ are independent of each other, the variance V ar(X (j) M ) can be further decomposed as:EQUATIONEQUATIONFinally, taking Eq. ( 18) into Eq. ( 13), the optimal σ 2 * p can be represented asEQUATIONWe validate our strategies on DynamiCrafter [63] (320 × 512 version), VideoCrafter1 [12] (320 × 512 version), and SVD [9] . All used codes in this paper and their licenses are listed in Tab. 3.Table 3 : Code Links and Licenses.Motion Score. The Motion Scores of DynamiCrafter [63] , VideoCrafter1 [12] and SVD are implemented following SVD [9] .Specifically, we compute the motion score by resizing the video to 800 × 450, extracting dense optical flow maps using RAFT [50] between adjacent frames, calculating the magnitude of the 2D vector for each pixel, averaging these magnitudes spatially, and then summing them across all frames.As for PIA [70] , we calculate the motion score by making a slight modification to the affinity score proposed in [70] , namely: motion score = 1 -affinity score.More specifically, for each video, we calculate the L1 distance between each frame v i and the condition frame v 1 in HSV space, which is denoted as d i . Then we apply this operation to all frames of video clips in the dataset and find the maximum distance value d max . We normalize the distance d i to [0, 1] via d max . Finally, the motion score for each frame can be calculated bym i = d i /d max × (m max -m min ), where m max and m min are hyperparameters set as 1, 0.2 respectively.User study. We ask users to compare ours with the baselines and determine which ones exhibit more dynamic and natural motion, greater temporal consistency, better alignment with the conditional image, and overall preference. In this section, we first examine commonly used strategies that involve adjusting the noise schedule to higher noise levels to bridge the training-inference gap [14, 27, 9, 32] , which, unfortunately, may exacerbate conditional image leakage. Following the SVD [9] , we increase noise by adjusting the P mean in the EDM framework [28] . Surprisingly, as shown in Fig. 10 , we observe that larger P mean values correspond to reduced motion in the generated videos. We hypothesize that this is due to the increased noise added to X t by larger P mean , making it more challenging to predict clean frames and thus more prone to conditional image leakage. Consequently, the synthesized videos tend to lack dynamic and vivid motion.In this section, we show another choice to add noise to the conditional image. Specifically, the noisy conditional image y s at time t is obtained byEQUATIONwhere β s ∼ p t (β s ), ϵ ∼ N (0, I), and β m = 1. As shown in Fig. 12 , this choice leads to video discoloration in SVD [9] .Diffusion Models for Image Generation. Recently, diffusion models have achieved significant breakthroughs in image, video, and 3D generation [24, 16, 42, 38, 5, 71, 37, 58, 59, 72] . For image generation, the latent diffusion model [42] addresses computational costs by leveraging VQ-VAE [53] to transform pixel-space images into compact latent representations, subsequently training diffusion models within this latent space. Building upon the latent space concept, subsequent works such as Require: the conditional image y 0 , the sampler for diffusion models Sampler(•, •, •) select the initial time step M ∈ (0, T ) calculate µ * p and σ 2 * p in initial noise distribution according to Eq. ( 5) X M ∼ N (X M ; µ * p , σ 2 * p I) for t = M, ..., 1 do X t-1 = Sampler(X t , y 0 , t) end for return X 0Require: the clean video X 0 , the conditional image y 0 , the noise schedule α t , σ t , the time-dependent noise distributionp t (β s ) repeat t ∼ U([0, T ]) β s ∼ p t (β s )▶ Sampling the noise level for the conditional image ϵ y ∼ N (0, I)y s = y 0 + β s ϵ y ▶ Adding noise to the conditional image ϵ ∼ N (0, I) X t = α t X 0 + σ t ϵ θ ← θ -η∇ θ ∥ϵ θ (X t , y s , t) -ϵ∥ 2 2until converged SDXL [39] , DALLE-3 [8] , and SD3 [18] have further enhanced the performance. By exploiting such advancements in text-to-image diffusion models, numerous methods have demonstrated promising results in text-driven controllable image generation and image editing [68, 22, 52] . As for image editing, studies such as Prompt-to-Prompt [22] and Plug-and-Play [52] have explored attention-based control mechanisms over generated content, consistently delivering impressive results. For image translation, EGSDE [71] and DiffuseIT [30] propose to employ an additional energy function to guide the inference process of a pre-trained diffusion model.Diffusion Models for T2V Generation. Approaches to T2V generation can be classified into two main categories. The first one involves directly generating videos based on text [46, 10, 23, 12, 13, 54, 67, 56, 9, 17] . For instance, as a pioneering endeavor, Make-A-Video [46] utilizes a pre-trained text-to-image model along with a prior network for T2V diffusion models, obviating the necessity for paired video-text datasets. VideoLDM [10] maintains the parameters of a pre-trained T2I model while fine-tuning the additionally introduced temporal layers. While many models are based on a U-Net [43] architecture, more recently, transformers have emerged as a foundational architecture for video generation due to their scalability [21, 36, 35] . The second category typically entails a two-step generation process: first generating an image based on the textual input and subsequently creating a video conditioned on the text and the generated image [31, 20] . For instance, Emu-video [20] initializes a factorized text-to-video model using a pre-trained text-to-image model and then fine-tunes temporal modules in the I2V stage.Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: The paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material. Guidelines:• The answer NA means that paper does not include experiments requiring code. • Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 
experimental setting/details: 
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 Appendix:  


Decision: This paper discusses the issue of conditional image leakage in an image-to-video generation model where the conditioning image may potentially bring unintended control into the generation of video dynamics. The paper proposes two ideas to mitigate the issue, leading to strong empirical performances. 

The paper was reviewed by four experts, who questioned various aspects of the paper. Authors provided a strong rebuttal, addressing the concerns, resulting in all reviewers inclining towards acceptance. AC agrees with the reviewers that the paper considers an important problem in conditional video synthesis, and the proposed approach shows solid results. Thus, AC recommends acceptance. Authors should revise the paper as per the reviewers' suggestions as well as incorporate all the comparisons that were provided in the rebuttal in the camera-ready.

Reviews: review: paper_summary: paper_summary: This paper presents an approach for image-to-video (I2V) generation. The authors start with a conditional image leakage problem in existing works where the conditional image significantly influences the generation process thereby impacting the dynamism of the video. The authors propose an inference time and a training time strategy to mitigate the problem. The approach is evaluated on UCF101 and the authors' ImageBench dataset. The results are impressive.
strength_weakness: strength_weakness: 1. The authors are encouraged to provide more details about the ImageBench dataset.

2. While performing the user study, what qualities are evaluated by the users?

3. How is the accuracy of the generated videos estimated corresponding to the text prompt?  Do the users rate the matching score of the generated video corresponding to the text prompt?

4. The realism of the generated videos is lacking. This is common for diffusion-based generated visual content.  Have the authors considered how to improve the realism of generated videos?
questions: questions: Please address the comments in the weaknesses section.
limitations: limitations: Yes
paper_summary: paper_summary: This paper investigates and proposes solutions for the problem of “conditional image leakage” in image-to-video generation. The authors claim that existing image-to-video diffusion models over-rely on the conditional image at large diffusion timesteps when the inputs are too noisy, leading to static video output. To address this issue, the authors propose an inference-time generation process that skips those large timesteps. The authors also propose adding noise to the conditional image during training to mitigate conditional image leakage.
strength_weakness: strength_weakness: There are a lot of prior works [a, b, c] that deal with noise initialization in video diffusion models that are highly related to the problem of conditional image leakage and the proposed inference technique. There are, however, no discussions on the differences between these techniques. Notably, FreeInit [a] and FrameInit [c] perverse the low-frequency component of the initial image – which is at odds with the proposed theory. I believe an in-depth discussion and a comparison with these approaches would help to establish the thesis of this paper. Additionally, PIA [d] also allows motion control – comparing the proposed motion-adding techniques with PIA’s motion control would also be helpful.

[a] FreeInit: Bridging Initialization Gap in Video Diffusion Models

[b] Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models

[c] ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation

[d] PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models

Secondly, I am not entirely convinced by the authors’ explanation of conditional image leakage. The authors claim that models learn a shortcut by copying the initial image at large diffusion timesteps, leading to static video output. However, models like VideoCrafter and SVD do produce outputs of diverse motion of some degree, as shown in their papers. What are the authors’ explanations for these motions? Where are they coming from? Trying to copy the conditional image when the input is otherwise noise is likely the optimal solution and would be learned – but that is not a sufficient condition for static output.

Thirdly, how are evaluations normalized for motion magnitude? Methods like SVD and PIA have motion control. Since the evaluation is focused on “dynamic motion”, perhaps choosing a different motion parameter for these methods would lead to different results in the user study. How are those parameters chosen? Do baseline SVD, SVD after finetuning, and SVD with the inference trick interpret the same motion scale parameter in the same way? One way to validate this is to plot the input motion scale parameter against the output motion magnitude (e.g., optical flow) for parameter selection.
questions: questions: What are the motion magnitudes of SVD in Figure 3 and Figure 7? 

Adding different noises to the conditioning image at different time steps requires the conditioning image to be embedded multiple times. How does this affect inference time?
limitations: limitations: The authors have adequately addressed the limitations.
paper_summary: paper_summary: The paper points out that existing image-to-video diffusion models can lead to videos without significant/desired amount of motion. Some evidence is presented to show that this is due to what the authors call "conditional image leakage" where the model places too much emphasis on the conditional image and all the generated frames look too similar to the conditional image. Two strategies are proposed to overcome this issue. The inference strategy starts the reverse diffusion process at an earlier time and uses improved initial noise distribution parameters using a closed-form expression to match the noise parameters at training time. The training strategy develops a time-dependent noise distribution to have higher noise at larger t to place lesser emphasis on the conditioning image for larger t values. Experiments indicate improved video generation using either or both of these strategies.
strength_weakness: strength_weakness: 1. Issues with derivation: For deriving the expression of better noise initialization, the authors present their proof in Appendix A. The second and third terms of (7) are considered constants for the purpose of optimizing the noise distribution. I don't follow this and think there may be a technical error here. It is written that since $q_M(X_M)$ and $p_M(X_M)$ are independent, the entropy $H(\mathcal{N}(X_M; \mathbf{\mu}_q, \Sigma_q))$ and $H(q_M(X_M))$ can be considered constants for optimizing the parameters $\mathbf{\mu}_q$ and $\Sigma_q$. I don't think this is correct as these parameters influence both $H(\mathcal{N}(X_M; \mathbf{\mu}_q, \Sigma_q))$ and $H(q_M(X_M))$. Can the authors please explain this in detail?

2. Quantifying motion: The authors say that with previous works the models could in principle just output the conditioning image several times and still lead to lower error or improved quality metrics like FID and IS. However, they still use the same metrics for their experiments. Perhaps quantifying amount of motion or even better, quantifying realism of motion directly is important. 

3. Figure 9 is not clear. What are orange and blue colors indicating. Please explain this figure in detail and how it shows that the proposed method is considered better by the user study. I see very little differences going from <method> to <method>-tune in the figure.
questions: questions: No additional questions.
limitations: limitations: Yes, authors have addressed some limitations of their method.
paper_summary: paper_summary: This paper argue that I2V-DMs tend to overly rely on the conditional image at large time steps, resulting in videos with less dynamic motion.
To address this, they propose using the KL divergence between the initial noise distribution and the actual marginal distribution to enhance the amplitude of motion while preserving video quality.
Besides，the paper propose a time-dependent noise distribution for the conditional image during training process.
strength_weakness: strength_weakness: 1. Claim. Regarding the conditional image and the diversity of content changes (referred to as 'motion' in this paper), it is, in fact, a matter of trade-off. The essence of image to video generation is to leverage the conditional image to enhance video quality. Therefore, the Proposition 1 and Training Strategy proposed by the authors are essentially fine-tuning techniques that favor the diversity of motion.

2. Additionally, the foundation model of video generation guided only by the first frame inherently contains uncertainty in its output; it does not know whether the user wants to generate a video with significant or minimal motion. Some recent studies (last frame guided[1], motion control[2])have demonstrated that incorporating additional guidance signals can indeed achieve big and precise motion. 

Particularly, the works DragNUWA and MotionCtrl have shown that for the image-to-video generation task, it is still possible to generate significant motion. Therefore, the foundation model of SVD has the potential to directly generate substantial motion. In this context, the initial version of SVD, which could only generate minor motion, might be due to the lack of a specific condition or guidance signal for significant motion, rather than an inherent incapability. From a subjective standpoint, not all applications require substantial motion.

[1] ToonCrafter: Generative Cartoon Interpolation
[2] MotionCtrl: A Unified and Flexible Motion Controller for Video Generation
[3] DragAnything: Motion Control for Anything using Entity Representation
[4] DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory 

3. Experiments. The experiments appear to be somewhat confusing and not entirely convincing. 
Regarding the variables of the related training strategy displayed in Figure 8, the reviewer has not found the corresponding ablation study, which is a core contribution of this paper. Additionally, the author has not provided an ablation study on the 'M' of Proposition 1, such as testing performance changes between 0.6T and 1T, which is central to the first contribution. If the reviewer has overlooked it, please point it out.

Additionally, Figure 9 also appears to be somewhat confusing. Was a user study not conducted on the Analytic-Init? What does '-tune' signify—does it mean finetuning (training strategy)? If so, why does it appear in the inference phase?

--------------------
After reading the author’s and other reviewers' feedback, I have adjusted my score to 5.
questions: questions: Can the authors provide more insights into the selection of hyperparameters (Line 230 Implementation Details) for the time-dependent noise distribution?
limitations: limitations: yes, the authors have discussed the limitations.

Comments: title: Thanks for the feedback
Further Response to Reviewer NwFJ
Thanks for the feedback
Further Response to Reviewer NwFJ
Thank you for your response
Author Response to Reviewer f9AW
Looking forward to further feedback
comment: Thank you for the feedback and for updating the score. We greatly appreciate your patience and the multiple rounds of communication, as your engagement has significantly enhanced the quality of our work.
Thank you for the responses. They addressed most of my concerns. I have increased the rating to weak accept.
We appreciate the reviewer’s quick feedback. Below, we address the further concerns.

## Q1: Details about the experiments across motion scores.

### Q1-1: Please detail the computation of the motion score
We compute the motion score by resizing the video to $800\times450$, extracting dense optical flow maps using RAFT [1] between adjacent frames, calculating the magnitude of the 2D vector for each pixel, averaging these magnitudes spatially, and then summing them across all frames.


[1] Raft: Recurrent all-pairs field transforms for optical flow.

### Q1-2: Why it would make sense to directly compare the input/output motion scores.

Training SVD involves learning a conditional probability distribution $p(X|y)$, where $y$ represents the motion score condition. During inference, the generated output $X' \sim p(X|y)$ is expected to match the given condition $y$. Therefore, the error between the motion score of generated output $y'$ and the given input score $y$ can reflect the discrepancy between the generated outputs and the target distribution in terms of motion magnitude.

We will include the above details in the main paper in the final version. 


## Q2: Comparisons with PIA: "it categorizes motion degree into three discrete levels (small/moderate/large) labeled as 0/1/2" -- this is not true. The motion value from PIA comes from pixel-level L1 distance in the HSV space which obviously has more than three levels.

Thanks for highlighting the misunderstanding about PIA. The default three levels (small / moderate / large) of motion degree in PIA correspond to three predefined input affinity scores $S _ {in} = $ {$s _ {in}^i $}$ _ {i=1}^n$ , which are negatively correlated with the motion score, where $n$ is the frame and $s _ {in}^i\in[0,1]$. Consistent with the experiments from Q1, we evaluate the precision of motion control by computing the error between the input and output affinity scores. We implement the output affinity scores $S _ {out} =$ {$s _ {out}^i$}$ _ {i=1}^n$ following the guidelines for calculating the input affinity scores as described in the PIA paper's Inter-frame Affinity section (3.2). Then error between them is computed as:
$$
    error = \sum_{i=1}^n \frac{|s_{in}^i - s_{out}^i|}{n}.
$$
As shown in **Rebuttal Table G**, our method consistently outperforms the baseline by achieving a lower affinity score error, regardless of the input affinity score. We will include the above experiments in the main paper in the final version. 

**Rebuttal Table G.** The error comparison between PIA and PIA with our Analytic-Init. The three motion degree levels of Input Motion Level correspond to three predefined input affinity scores.
|Input Motion Level|Error(PIA/Ours)$\downarrow$|
|--|--|
|Small Motion|0.309/**0.257**|
|Moderate Motion|0.230/**0.221**|
|Large Motion|0.182/**0.124**|
Thank you for the clarification. I think Table A and the analysis of input/output motion scores provide important support for the claims made in the paper. Please include them in the main paper. Particularly, please detail the computation of the motion score, that SVD has been trained with such scores, and why it would make sense to directly compare the input/output motion scores. I understand them now but it was not quite clear.

Comparisons with PIA: "it categorizes motion degree into three discrete levels (small/moderate/large) labeled as 0/1/2" -- this is not true. The motion value from PIA comes from pixel-level L1 distance in the HSV space which obviously has more than three levels.
Thank you for the appreciation of our response and the update on the score. We highly appreciate it.
We sincerely appreciate the reviewer’s feedback. Below, we address the further concerns in detail.

## Q1: Where is "output motion score" defined? 

Sorry for the unclear description. Since the official code is not available, we implement the output motion score following the guidelines for calculating the input motion score as detailed in the Optical Flow section (Appendix C) of the SVD paper. This approach ensures consistency in the calculation of both the input and output motion scores. Consequently, this consistency enables the error to accurately reflect the precision of motion control achieved.

## Q2: Some tuning has been done for the proposed method  so it would be fair to perform similar tuning for SVD and the baselines.

As presented in Rebuttal Table A, we conduct tuning for the input motion score of the two baselines (SVD and SVD-naive-tune) within the range of 5 to 200, including the default score of 127. The results indicate that our method consistently outperforms these baselines, achieving both higher user preference and lower motion score error, regardless of the input motion score.

## Q3: A lower error suggests that the input motion score is supposed to be calibrated to the output motion score. I am not sure how the output motion score is defined. Comparisons with PIA -- Again, this does not show how the input motion score for PIA is selected.

As addressed in our response to Q1, we ensure consistency between the input and output motion scores by implementing the output motion score calculation following the guidelines from the SVD paper. Regarding PIA, since it categorizes motion degree into three discrete levels (small/moderate/large) labeled as 0/1/2, it's not feasible to compute a precise motion score error. Nevertheless, as presented in Rebuttal Table D of the response PDF, regardless of the input motion level, our method consistently outperforms PIA in user preference metrics. Combined with the main baseline SVD results in Rebuttal Table A, these findings underscore the effectiveness of our approach.

We sincerely appreciate the reviewer’s constructive suggestions and believe that the additional experiments, analysis, and explanations significantly improve the quality of our submission. We hope that this provides a sufficient reason to consider raising the score.
I thank the authors for their response. 

I under the derivation better now, I think misunderstood some parts initially. 

The additional results quantifying motion could be of value in the final paper, if accepted. And they are appreciated.

Overall, the authors have addressed my concerns, and I believe they have addressed most of the other reviewers' concerns.

I am raising my score to a 6 based on NeurIPS rating scale.
I thank the authors for the response.
Follow-up questions:
- Where is "output motion score" defined? I cannot find it in the rebuttal and the mentions of "motion score" in the main paper seem to be all related to the input, not the output.
- Q3 -- The authors chose a score of 20, much lower than the default 127. The authors claim that this "achieves high visual quality". Does this suggest a score of 127 does not achieve high visual quality? Can we back this up? If so, why not 15, or 25? Some tuning has been done for the proposed method (e.g., Table 8) so it would be fair to perform similar tuning for SVD and the baselines.
- Also Q3 -- "consistently achieving a lower error between the motion score of outputs and input motion score" -- suggests that the input motion score is supposed to be calibrated to the output motion score. Again I am not sure how the output motion score is defined.
-- Comparisons with PIA -- Again, this does not show how the input motion score for PIA is selected.
Dear Reviewer f9AW,

Thank you for your feedback. We are pleased to hear that our responses have addressed most of your concerns. We believe the additional experiments, analysis, and explanation have significantly improved the quality and clarity of our submission. We hope that you and other reviewers may regard this as a sufficient reason to raise the score.

Best, Authors
Thank you for the author's response, which addressed most of my concerns. I will finalize my score after discussing with other reviewers, and it will likely be between 4 and 5. Thanks.
Dear AC and Reviewers,

Thank you again for the great efforts and valuable comments. We have carefully addressed the main concerns in detail. We hope you might find the response satisfactory. As the discussion phase is about to close, we are very much looking forward to hearing from you about any further feedback. We will be very happy to clarify any further concerns (if any).

Best, Authors