EditVerse: Unified Multimodal Editing

• EditVerse is a unified multimodal framework that enables cross-modal image and video editing through in-context learning and token sequence representation.
• It leverages a single transformer architecture to process text, images, and videos via a common token embedding, enhancing flexibility and performance.
• The framework incorporates scalable data curation and introduces EditVerseBench to rigorously evaluate instruction-based edits, ensuring high fidelity and temporal consistency.

EditVerse denotes a unified multimodal framework and research direction for general-purpose image and video generation and editing via in-context learning. Anchored in a single transformer architecture capable of representing and manipulating text, images, and videos as an interleaved token sequence, EditVerse enables cross-modal transfer, flexible instruction handling, and state-of-the-art performance on instruction-based generation and editing tasks across arbitrary formats and durations. The framework incorporates scalable data curation for video editing and proposes benchmark protocols for comprehensive quantitative and user-driven assessment. Its emergent capability to generalize and execute editing operations outside the explicit training distribution marks a significant advance in convergent multimodal AI.

1. Unified Multimodal Model Architecture

EditVerse is architected as a single model able to process and generate outputs for both image and video editing/generation by jointly representing all modalities as a linear token sequence. Text, images, and videos are mapped via individual encoders and projectors into a common token embedding space; the full input—including instructions and content to be edited—is serialized as a contiguous, interleaved sequence. Each image or video is first compressed into latent patch tokens using a convolutional variational autoencoder (VAE), while text instructions are tokenized (e.g., by a Flan-T5-XXL encoder) and converted to the same dimensionality via a linear projector. The entire sequence, demarcated with special segment tokens for vision content, is then processed by a transformer with full self-attention, enabling explicit cross-modal interactions at every layer. The model's flexibility in sequence length and content type accommodates variable resolutions, temporal durations, and mixed instructions without architectural modification (Ju et al., 24 Sep 2025).

2. Token Sequence Representation and Attention

The design core of EditVerse is the unified one-dimensional token representation for all supported modalities. A vision encoder converts visual content (both images and video frames) into latent token grids, preserving spatial and temporal structure as a series of tokens. Text tokens, after projection, are inserted at specific places in the sequence, interleaved as required by the desired editing/generation task. For video, latency and scalability are addressed by using spatio-temporally compressed patches with carefully assigned positional encodings. Importantly, self-attention operates globally across the entire input, supporting long-range dependencies not only within but also between modalities. This facilitates in-context learning—the ability to condition on arbitrary context, instruction, and history during inference—and enables the model to learn transfer functions between image and video domains despite data imbalances. Special tokens (e.g., Start-of-Vision, End-of-Vision) delineate content segments and assist with sequence parsing during training and inference (Ju et al., 24 Sep 2025).

3. Data Curation Pipeline and Multimodal Training

Recognizing the scarcity of high-quality video editing datasets, EditVerse introduces a scalable, automated data pipeline to synthesize and filter diverse video editing samples. The pipeline is comprised of:

• Segmentation using models such as Grounded-SAM-2 to extract object masks from video frames.
• Application of generative models for object erasure (DiffuEraser), VACE-based inpainting for content modification, and vision-LLM-driven transformations for tasks such as style transfer or object substitution.
• Propagation techniques to ensure that edits applied to individual frames are consistently mapped across full video clips.
• Integration of public datasets (e.g., Señorita-2M) with a multi-stage filtering process using vision-LLMs to assess edit fidelity, context preservation, visual sharpness, and temporal consistency.

Through this pipeline, EditVerse amasses over 232,000 high-quality video editing examples, paired with complementary large-scale image generation/editing and video generation data, thus ensuring robust joint training and effective cross-modal knowledge transfer (Ju et al., 24 Sep 2025).

4. Benchmarking: EditVerseBench

To standardize and advance the evaluation of instruction-based video editing, EditVerse introduces EditVerseBench—the first comprehensive benchmark covering diverse video editing tasks. Key details:

• Contains 100 thoroughly vetted video clips (equal parts horizontal and vertical orientation), each stratified into 20 editing categories and paired with dual prompts (source and target) plus an explicit instruction.
• Assessments leverage a vision-LLM–based editing score, frame-wise PickScore for quality, alignment metrics (CLIP for frames, ViCLIP for overall video/instruction match), and temporal consistency analyses (using CLIP and DINO).
• The benchmark supports both quantitative model comparison and human preference studies, enabling rigorous and multi-dimensional evaluation of editing abilities.

EditVerseBench serves as an essential resource for systematic progress tracking in instruction-based video and image editing (Ju et al., 24 Sep 2025).

5. Performance, Emergent Abilities, and Cross-Modal Transfer

Extensive experimental evaluation demonstrates EditVerse's superiority over both open-source and commercial systems in editing quality, text-image/video alignment, and generation fidelity. Specific advances include:

• State-of-the-art performance on EditVerseBench, TGVE+, and V2VBench benchmarks with significant improvements in VLM-based editing scores, frame and video quality, and alignment consistency.
• Superior outcomes on user studies, with generated content rated higher for instruction faithfulness, preservation of unedited regions, and overall visual appeal.
• Ablation reveals the critical role of interleaved token representation and novel four-dimensional rotary positional encodings (encompassing sequential, temporal, height, and width axes) for high editing fidelity.
• During training, the model employs a Flow Matching loss for denoising diffusion: for clean target $X_1$ and noisy input $X_0 \sim \mathcal{N}(0,1)$, intermediate state $X_t = tX_1 + (1-t)X_0$, and loss $$\mathcal{L} = \mathbb{E}_{t, (X_0, X_1)} \|u_\Theta(X_t, t) - (X_1 - X_0)\|^2$$.

A notable emergent property is EditVerse's capacity to perform editing operations beyond its training distribution: for example, effecting novel scene transformations or stylistic changes absent from explicit training categories—attributed to its unified token architecture and cross-modal learning (Ju et al., 24 Sep 2025).

6. Significance, Limitations, and Future Directions

EditVerse operationalizes the long-held objective of unified, foundation-model–driven editing and content generation across modalities. Its token-sharing, context-rich transformer design achieves robust in-context learning, transfer from abundant image data to sparse video editing distributions, and generalizes editing behaviors across instructions and content types. Limitations include ongoing dependency on effective automated video editing curation and the need for further scale to cover finer-grained or domain-specific editing tasks.

Potential future developments include extending EditVerse to additional modalities (audio, 3D), exploring more efficient attention mechanisms for scaling to longer or higher-resolution sequences, and leveraging its in-context learning for fully user-driven or real-time multimodal editing workflows. This framework provides a foundation for the convergence of editing/generation platforms into a single, multimodal model paradigm capable of emergent, general-purpose behavior.