Pathways on the Image Manifold: Image Editing via Video Generation

Noam Rotstein; Gal Yona; Daniel Silver; Roy Velich; David Bensaïd; Ron Kimmel

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Pathways on the Image Manifold: Image Editing via Video Generation

Noam Rotstein, Gal Yona, Daniel Silver, Roy Velich, David Bensaïd, Ron Kimmel

arXiv:2411.16819v4Nov 25, 2024cs.CVcs.AIcs.LG

TL;DR

Image editing methods can struggle to follow complex instructions while preserving important source content. Frame2Frame reformulates editing as a temporal video-generation process with captioning, coherent sequence generation, and frame selection. The paper reports state-of-the-art editing results while maintaining high source-image fidelity, though camera motion, training-domain coverage, computational efficiency, and subjective frame choice remain constraints.

Problem
Existing image-editing methods struggle to achieve precise edits while preserving essential characteristics of the original image.
Method
Frame2Frame uses a temporal editing caption, an image-to-video generator, and VLM-assisted frame selection to transform the source image through coherent intermediate states.
Results
Frame2Frame achieves state-of-the-art editing results while maintaining high fidelity to source images across standard benchmarks and human-pose editing evaluation.
Takeaways & Limitations
Recasting image editing as video generation provides a continuous image-manifold path for natural, temporally coherent transformations.
Takeaways & Limitations
The approach can exhibit unintended perspective shifts from camera motion, remains constrained by video-model training domains, uses resource-intensive generation, and requires subjective frame selection.

Abstract

from arXiv · show

Recent advances in image editing, driven by image diffusion models, have shown remarkable progress. However, significant challenges remain, as these models often struggle to follow complex edit instructions accurately and frequently compromise fidelity by altering key elements of the original image. Simultaneously, video generation has made remarkable strides, with models that effectively function as consistent and continuous world simulators. In this paper, we propose merging these two fields by utilizing image-to-video models for image editing. We reformulate image editing as a temporal process, using pretrained video models to create smooth transitions from the original image to the desired edit. This approach traverses the image manifold continuously, ensuring consistent edits while preserving the original image's key aspects. Our approach achieves state-of-the-art results on text-based image editing, demonstrating significant improvements in both edit accuracy and image preservation. Visit our project page at https://rotsteinnoam.github.io/Frame2Frame.

1. Introduction

Image editing methods often struggle to execute precise edits while preserving important source content. Frame2Frame reformulates editing as temporally coherent video generation, using intermediate states and automated frame selection to improve both goals.

1. Introduction: Existing text-guided diffusion methods must produce one image that simultaneously satisfies complex edit instructions and preserves source fidelity, often compromising one objective.These methods condition generation on the source image and target description through techniques such as latent inversion [32] or model fine-tuning [22].
1. Introduction: Frame2Frame reformulates image editing as video generation, creating a continuous path from the source image toward the target edit.The approach uses temporal coherence and physically plausible intermediate states rather than a single-state transition.
1. Introduction: The method is designed to leverage video models’ temporal coherence and world understanding to preserve source characteristics while carrying out high-fidelity edits.Its geometric interpretation is a continuous traversal along the natural image manifold instead of projection to a single target point.
1. Introduction: F2F converts an edit instruction into a Temporal Editing Caption, generates a coherent video with an image-to-video model, and selects the best edited frame with a VLM.This three-stage pipeline combines temporal captioning, video-based editing, and automated frame selection.
1. Introduction: The paper reports state-of-the-art performance on TEdBench and introduces PosEdit for evaluating human pose edits.These benchmarks form part of the paper’s comprehensive evaluation of the proposed framework.

2. Related Efforts

Image editing builds on text-guided diffusion but lacks predefined source-target ground truth, motivating diverse editing strategies. Meanwhile, video generation has evolved into high-fidelity systems with temporal and physical coherence, and prior work has only limitedly connected video to image editing.

2. Related Efforts: Image editing lacks predefined source-target ground-truth pairs, motivating methods such as noise-based editing, model fine-tuning, and video-derived training data.Examples include SDEdit [30], which injects noise before denoising toward an edit prompt, and Imagic [22], which fine-tunes on one image.
2. Related Efforts: Video generation has progressed from short, domain-specific clips to diverse high-fidelity models built with large datasets and latent diffusion architectures.The literature includes Stable Video Diffusion [2] and Sora [28], whose systems are described as world simulators because of temporal and physical understanding.
2. Related Efforts: Prior computer-vision applications use video diffusion models for tasks such as novel-view synthesis and 3D scene or object modeling.Make-A-Video3D [41], ViVid-1-to-3 [25], and PhysDreamer [51] transfer video-model capabilities to related visual problems.
2. Related Efforts: F2F’s manifold-pathway visualization depicts temporally consistent intermediate states that preserve the “AI” shirt text while reaching the requested pose edit.The example contrasts the continuous path with a competing approach that loses the shirt text.
2. Related Efforts: The intersection of image editing and video has received limited attention, with existing approaches mainly sampling video-frame pairs to construct image-pair datasets.AnyDoor [8] uses paired frames for object-editing augmentation, while MagicFixup [1] uses them to build a refinement-focused dataset.

3. Frame2Frame

Frame2Frame reformulates image editing as a temporal transformation, generating a coherent video path from source to target and selecting the frame that best realizes the edit.

3. Frame2Frame: Frame2Frame generates natural transitions between source and target images, using video generation to create consistent edits.
3.1. Temporal Editing Caption: The pipeline constructs a Temporal Editing Caption describing how the edit unfolds over time from the source image and target caption.
3.2. Video Generation: A pretrained image-to-video model uses the source image and temporal caption to generate a temporally coherent sequence that evolves along the image manifold.
3.3. Frame Selection: Frame selection chooses the earliest frame that achieves the desired edit because later frames may overrun the required transformation.
3.3. Frame Selection: The optimal edited frame is subjective and may vary with user preferences, making frame selection flexible across interpretations of the target edit.
3. Frame2Frame: Figure 3 summarizes the three-stage workflow: temporal caption generation, video generation, and automated selection of the final edited frame.

4. Editing Manifold Pathway

The editing manifold pathway models semantic image changes as smooth movement through a natural-image manifold, preserving source attributes during target-directed edits.

4. Editing Manifold Pathway: The manifold visualization uses three 200-image FLUX.1-dev sample sets to compare source, combined-edit, target, and noise distributions.
4. Editing Manifold Pathway: Smooth manifold traversal can change a person’s pose while preserving the text on the person’s shirt.
4. Editing Manifold Pathway: Table 1 evaluates content preservation with LPIPS and CLIP-I and edit accuracy with CLIP, reporting better or comparable F2F performance across metrics.
4. Editing Manifold Pathway: Conventional single-image editing may move abruptly to a different semantic cluster and remove source attributes such as shirt text.

5. Experiments

Frame2Frame is evaluated against established and newly introduced benchmarks using quantitative, qualitative, and human-preference protocols. Across these evaluations, it improves edit accuracy while preserving source content and identity.

5. Experiments: The evaluation compares Frame2Frame with LEdits++, SDEdit, Pix2Pix-Zero, Imagic, and FlowEdit across TEdBench and PosEdit.Methods use consistent protocols, including manual selection of the best result from multiple random seeds and default official settings.
5.2. TEdBench Evaluation Results: Qualitative TEdBench comparisons show edits that better follow target prompts while preserving source content and structure, including complex teddy-bear modifications.Figure 4 presents comparisons across diverse editing tasks.
5.2. TEdBench Evaluation Results: On TEdBench, Frame2Frame balances source preservation and prompt alignment using LPIPS, CLIP-I, and CLIP score.Lower LPIPS and higher CLIP-I indicate better source preservation, while higher CLIP score indicates better adherence to the target prompt.
5.3. PosEdit Benchmark: Frame2Frame consistently outperformed competitive methods across PosEdit metrics, especially similarity to the ground-truth target image.The benchmark evaluates source preservation, prompt-based edit accuracy, target similarity, and identity preservation using reference images.
5.4. Human Evaluation Survey: F2F achieved 53% versus LEdits++’s 47% global preference for edit quality and 65.6% versus 34.4% for edit accuracy in the human survey.Per-image preferences followed the same trend, with no significant influence from outliers.

6. Limitations

Frame2Frame introduces camera-motion artifacts, domain-related difficulty, and substantial computational cost. Its current 49-frame CogVideoX process can be slower and more resource-intensive than other image-editing methods.

6. Limitations: Video generation makes Frame2Frame computationally intensive and often slower than other image-editing methods.The authors note that converting an image into a video sequence is resource-heavy.
6. Limitations: Natural camera motion can introduce unintended perspective shifts in generated edits.This occurs when camera motion appearing in the video sequence is replicated in the generated content.
6. Limitations: Video models may struggle with edits that deviate substantially from their training data, which predominantly contains real-world transformations.The limitation follows from the data domains represented during video-model training.
6. Limitations: CogVideoX currently fixes each edit at 49 frames, while reducing the frame count could enable faster editing.Newer video models can execute the process in seconds, making the approach increasingly less resource-intensive.

7. Conclusions

Frame2Frame reformulates image editing as temporal transformation through video generation, producing natural source-to-target transitions. The paper reports state-of-the-art editing results while maintaining source-image fidelity and outlines efficiency and editing-specific video-model improvements as future directions.

7. Conclusions: Frame2Frame reformulates image editing as a temporal transformation process that uses video generation to create natural transitions between source and target images.The framework leverages video models to produce consistent and realistic edits.
7. Conclusions: The method achieves state-of-the-art results on standard benchmarks, introduces PosEdit for human pose editing, and shows promising results on classical vision tasks.
7. Conclusions: Future work includes fine-tuning video generators for image editing, enforcing static-camera settings, curating editing datasets, and reducing full-video-generation overhead.The current CogVideoX setup generates a fixed 49 frames per edit, and efficiency may depend on per-frame speed or the number of frames.
7. Conclusions: The appendix provides additional TEdBench examples, generated video sequences, and temporal editing captions illustrating the method’s editing process.

B.2. Ablation

The ablation evaluates temporal editing captions and automated frame selection as components of Frame2Frame. Temporal captions preserve similar source resemblance but improve editing performance, while selection favors the earliest frame satisfying the edit.

B.2. Ablation: Using target editing captions directly preserves similar source-image resemblance but underperforms temporal editing captions on image-editing performance.The comparison is reported in Table S4 under otherwise unchanged experimental protocols.
B.2. Ablation: The VLM selects the earliest frame that fully satisfies the editing intent, minimizing deviation from the original image.If no edited frame fulfills the edit, the original image is retained.
B.2. Ablation: Frame selection is evaluated against a target-edit caption using a collage of uniformly sampled video frames and the source image.The instruction asks the VLM to choose among 12 edited images or select image 0 when none follows the edit.

C.2. Ablation

This ablation tests whether selecting an intermediate frame is preferable to using the generated video’s final frame. The naive last-frame strategy lowers target CLIP score, while the broader evaluation includes pose-editing data and human judgments of edit accuracy and source fidelity.

C.2. Ablation: The naive strategy of using the last generated frame produces a lower target CLIP score than the proposed frame-selection method.This comparison is reported in Table S5.
C.2. Ablation: The human-pose editing dataset contains 58 tasks across 8 action categories and 8 subjects, with neutral standing sources and varied target poses.
C.2. Ablation: The human evaluation compares Frame2Frame with LEDITS++ on edit accuracy and visual-fidelity preservation through participant judgments.Each participant reviewed 20 edits.

H. Further Ablations

Further ablations test background variation and edits outside ordinary video distributions. Frame2Frame successfully handles both settings, including spontaneous unrealistic transformations shown in the supplementary examples.

H. Further Ablations: Frame2Frame successfully handles diverse backgrounds and out-of-distribution transformations in the additional ablations.The experiments include images of two objects placed in varying settings and unrealistic edit processes.
H. Further Ablations: The tested unrealistic edits include a teddy bear ripping open to reveal stuffing and a tennis ball transforming into a ripe red tomato.The supplementary video sequences show these changes occurring seamlessly without external interaction.

I. Additional Vision Tasks Captions

The framework is applied to additional classic vision tasks beyond conventional image editing, using Runway Gen-3 and longer, more descriptive temporal-editing captions.

The framework extends to classic vision tasks outside typical image editing, with Runway Gen-3 used as the video generator.
These tasks require longer and more descriptive captions than the temporal editing captions used elsewhere.
Captions: The section specifies temporal-editing captions for tasks including gradual relighting toward nighttime.