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Tuning-Free Noise Rectification for High Fidelity Image-to-Video Generation
Weijie Li, Litong Gong, Yiran Zhu, Fanda Fan, Biao Wang, Tiezheng Ge, Bo Zheng
TL;DR
Open-domain I2V methods can generate dynamic content but struggle to preserve reference-image fidelity, amid losses of image detail and biased noise predictions. This paper adds noise to the image latent and rectifies predicted noise during denoising using pre-trained video diffusion models. The authors report higher fidelity, alongside some loss of motion intensity.
Problem
Open-domain I2V methods can generate dynamic content but struggle to maintain fidelity, with image-detail loss and denoising noise-prediction bias identified as factors.
Source: §1 · p. 1Method
The method adds noise to the reference image latent, then rectifies predicted noise during denoising using initial noise as a pivotal reference.
Source: §1 · p. 2, §4.2 · p. 5Results
The authors report that the method achieves higher fidelity while losing some motion intensity.
Source: §6 · p. 9Takeaways & Limitations
The tuning-free, plug-and-play method can be applied to pre-trained video latent diffusion models for open-domain I2V.
Source: §1 · p. 2, §6 · p. 9The method achieves higher fidelity while losing some motion intensity.
Source: §6 · p. 9
Abstract
from arXiv · showhide
Image-to-video (I2V) generation tasks always suffer from keeping high fidelity in the open domains. Traditional image animation techniques primarily focus on specific domains such as faces or human poses, making them difficult to generalize to open domains. Several recent I2V frameworks based on diffusion models can generate dynamic content for open domain images but fail to maintain fidelity. We found that two main factors of low fidelity are the loss of image details and the noise prediction biases during the denoising process. To this end, we propose an effective method that can be applied to mainstream video diffusion models. This method achieves high fidelity based on supplementing more precise image information and noise rectification. Specifically, given a specified image, our method first adds noise to the input image latent to keep more details, then denoises the noisy latent with proper rectification to alleviate the noise prediction biases. Our method is tuning-free and plug-and-play. The experimental results demonstrate the effectiveness of our approach in improving the fidelity of generated videos. For more image-to-video generated results, please refer to the project website: https://noise-rectification.github.io.
1. Introduction
Open-domain image-to-video generation must retain the reference image while producing motion, but existing diffusion-based approaches struggle with fidelity. The paper addresses missing image detail and denoising noise bias with a tuning-free process that adds noise to the image latent and rectifies predicted noise.
1. Introduction
- Earlier image-animation approaches were often limited to specific domains or simple scenarios, making them difficult to apply to open-domain images.The cited examples include faces, human poses, simple natural scenes, fonts, drawings, and rigid objects.Source: §1 · p. 1
- Existing diffusion-based I2V methods [4] [11] [51] [59] can generate motion but struggle to maintain fidelity because image details are lost and denoising predictions are biased.The paper attributes detail loss to partial image representations and noise bias to the inability to achieve perfect training loss.Source: §1 · p. 1
- The method combines image-latent noising with rectification of predicted noise during denoising, using initial noise as a pivotal reference and a step-adaptive intervention to control image retention.It is designed to use pre-trained video latent diffusion models without additional training and to integrate in a plug-and-play manner.Source: §1 · p. 2
- The method’s comparisons with public I2V works and community attempts demonstrate higher-fidelity video generation in open domains.The approach uses existing pre-trained video diffusion models and requires no extra training.Source: §1 · p. 2
2. Related Work
The related work traces diffusion-based image and video generation, then reviews image-conditioning approaches for I2V. It positions the paper’s noise rectification as a tuning-free alternative intended to maintain fidelity.
2. Related Work
- The section frames video diffusion as a development drawing on image-generation progress and its methods for producing realistic textures and fine details.It introduces related work across image and video generation.Source: §2 · p. 2
2.1. Image Generation with Diffusion Model
- Image diffusion uses U-Nets to predict noise, while latent diffusion models encode images with a VAE and perform diffusion in latent space; ControlNet, T2I-Adapter, and IP-Adapter add image-generation control.SDEdit adds noise to an input image and progressively denoises it; the paper draws inspiration from image-editing transition refinement.Source: §2.1 · p. 2
2.2. Video Generation with Diffusion Model
- Video generation builds on text-to-video models, often using latent-space diffusion and temporal modules, while I2V methods condition on images through approaches such as ControlNet, IP-Adapter, or image-latent and feature injection.The paper notes that existing image-conditioned video methods either have limited fidelity or require fine-tuning the VLDM.Source: §2.2 · pp. 2–3
3. Preliminary
The preliminary section defines text-conditioned I2V as generating a video from a reference image and text, then outlines diffusion and video-latent-diffusion models. It describes their forward and reverse processes and the temporal modeling used for video.
3.1. Image-to-Video Task Definition
- Text-conditioned I2V generates a video from a specified image and text, aiming to preserve the image’s appearance while aligning the result with the text description.The section contrasts I2V’s emphasis on image fidelity with T2V and its reliance on prior knowledge or supplied conditions for motion.Source: §3.1 · p. 3
3.2. Video Latent Diffusion Models
- Diffusion models gradually add Gaussian noise to data over timesteps, then learn a reverse process that denoises from a noisy prior toward the desired data.The reverse model predicts a mean and variance, while the noise-prediction objective uses actual and predicted noise with text, image, or other conditions.Source: §3.2 · p. 3
- Video latent diffusion models encode video data into latent space and use temporal modules, often inflating a 2D U-Net to a 3D U-Net, to model frame dynamics.The described architecture treats frames as a batch axis in spatial modules and spatial dimensions as a batch axis in temporal modules.Source: §3.2 · pp. 3–4
- Figure 3 depicts the proposed pipeline: noise the input image into the initial latent, then rectify the inflated 3D U-Net’s predicted noise during denoising.Its intermediate-step visualization is described as bringing results closer to the given image.Source: Figure 3 · p. 4
4. Method
The method injects the reference-image latent into initial noise and rectifies predicted noise during denoising to improve fidelity. It addresses limitations of existing image-conditioning strategies, and the paper reports higher fidelity than current I2V methods in its visual comparison.
4.1. Enhance Image Condition Analysis
- Initial-noise conditioning yields resemblance rather than high fidelity; concatenation adds detail but requires retraining, while extracted features can lose fine details.Feature-based methods can capture overall style or layout while missing fine image details.Source: §4.1 · pp. 4–5
- Prior denoising can leave generated frame latents deviating from the image latent because predicted noise differs from true noise and biases accumulate.The passage attributes the discrepancy to training not achieving a perfect noise-prediction loss.Source: §4.1 · p. 5
4.2. Noise Rectification Strategy
- The method injects the reference-image latent into initial noise, then adaptively compensates predicted noise with known initial noise during intermediate denoising steps.This is intended to alleviate the accumulated noise gap and preserve fine-grained image details.Source: §4.2 · pp. 5–6
- A rectification weight balances first-frame and subsequent-frame noise gaps into an offset used to update predicted noise framewise.The offset calibrates predicted noise through the paper's key rectification procedure.Source: §4.2 · p. 5
- Figure 4 reports higher fidelity in generated sequences with the given image for this method than for current image-to-video methods.The comparison uses AnimateDiff as the video latent diffusion model.Source: Figure 4 · p. 6
- Adjusting rectification steps τ and weight ω controls fidelity retention; the method is tuning-free and applicable to most current video diffusion models.The method is presented as plug-and-play.Source: §4.2 · p. 6
5. Experiments
The experiments compare noise rectification with existing image-to-video approaches, test its adjustable settings, and extend it across video diffusion models. They report improved fidelity, with a trade-off between fidelity and motion intensity.
5.1. Experimental Setup
- Evaluation uses 1,000 WebVid10M video-text pairs and 1,000 LAION-Aesthetic image-text pairs, assessing fidelity, temporal coherence, and video-text alignment.For WebVid10M, the 10th video frame serves as the image input; LAION-Aesthetic is used for qualitative evaluation.Source: §5.1 · pp. 6–7
5.2. Comparisons
- With IP-Adapter, the method achieves the highest video-image fidelity (0.8042) and temporal coherence (0.9934) in the WebVid comparison.The authors also report acceptable video-text alignment while focusing on high-fidelity image-to-video generation.Source: Table 1 · p. 7, §5.2 · pp. 7–8
- The comparisons include input-layer image conditioning, intermediate-layer image injection, and methods combining both approaches.The listed baselines include SDEdit, ConcateImage, ControlNet Reference-Only, IP-Adapter, VideoCrafter1-I2V, VideoComposer, and I2VGen-XL.Source: §5.2 · p. 7
- Qualitatively, the method preserves more input-image detail than the compared approaches, while initial-only conditioning mainly retains style.Methods that iteratively use image information preserve more visual features than methods that add the condition only at the start.Source: §5.2 · p. 7
5.3. Ablation Study
- Frame-specific rectification weights affect fidelity and temporal consistency; the authors select the second tested setting for high fidelity and natural motion.The third and fourth tested settings may produce abrupt changes in image detail or motion effects.Source: Figure 5 · p. 7, §5.3 · p. 8
- Rectification from the start of denoising improves fidelity; the experiments use τ = [0%, 60%] to balance fidelity and motion intensity.Later-only rectification still yields poor fidelity, while extending rectification from τ = [0%, 20%] to τ = [0%, 100%] gradually improves it.Source: §5.3 · pp. 8–9, Figure 6 · p. 8
- Applied to AnimateDiff, ModelScopeT2V, and Hotshot-XL, the method maintains high fidelity and consistent animation.The extension uses each model’s pretrained motion prior without tuning.Source: Figure 7 · p. 8, §5.3 · p. 9
6. Conclusion
The conclusion presents a tuning-free noise-rectification method for high-fidelity open-domain image-to-video generation, while noting that higher fidelity comes with some loss of motion intensity.
6. Conclusion
- The proposed noising and rectified-denoising method is tuning-free and plug-and-play across video latent diffusion models, but higher fidelity comes with some loss of motion intensity.The authors identify increasing motion intensity while maintaining high fidelity as future work.Source: §6 · p. 9