Region-Constraint In-Context Generation for Instructional Video Editing (2512.17650v1)

Abstract: The In-context generation paradigm recently has demonstrated strong power in instructional image editing with both data efficiency and synthesis quality. Nevertheless, shaping such in-context learning for instruction-based video editing is not trivial. Without specifying editing regions, the results can suffer from the problem of inaccurate editing regions and the token interference between editing and non-editing areas during denoising. To address these, we present ReCo, a new instructional video editing paradigm that novelly delves into constraint modeling between editing and non-editing regions during in-context generation. Technically, ReCo width-wise concatenates source and target video for joint denoising. To calibrate video diffusion learning, ReCo capitalizes on two regularization terms, i.e., latent and attention regularization, conducting on one-step backward denoised latents and attention maps, respectively. The former increases the latent discrepancy of the editing region between source and target videos while reducing that of non-editing areas, emphasizing the modification on editing area and alleviating outside unexpected content generation. The latter suppresses the attention of tokens in the editing region to the tokens in counterpart of the source video, thereby mitigating their interference during novel object generation in target video. Furthermore, we propose a large-scale, high-quality video editing dataset, i.e., ReCo-Data, comprising 500K instruction-video pairs to benefit model training. Extensive experiments conducted on four major instruction-based video editing tasks demonstrate the superiority of our proposal.

• The paper introduces a novel in-context generation framework (ReCo) that integrates latent and attention constraints to perform precise, high-fidelity instructional video editing.
• The paper demonstrates robust performance across various editing tasks, outperforming baselines with superior metrics in local object manipulation and style transformations.
• The paper provides ReCo-Data, a 500K instruction-video pair dataset, enabling scalable training and rigorous benchmark evaluation for advanced video editing.

Region-Constraint In-Context Generation for Instructional Video Editing

Introduction and Motivation

The paper presents ReCo, a novel framework for instructional video editing driven solely by textual instructions. In contrast to earlier approaches relying on pixel-level masks or explicit region controls, ReCo introduces an in-context learning paradigm where region-based constraints are imposed in both latent and attention spaces. This enables fine-grained, high-fidelity editing—including local object manipulations and global style transformations—without explicit region annotation or custom task configuration, addressing major hurdles in both real-world deployment and scalable training.

Figure 1: ReCo supports complex video edits guided solely by text, spanning local and global modifications with high fidelity.

The framework’s technical advances are complemented by the introduction of ReCo-Data, a large, balanced dataset of 500K instruction-video pairs, constructed using a multi-stage pipeline that leverages state-of-the-art VLLMs for filtering and captioning, as well as robust synthetic video generation procedures.

Methodology

In-Context Generation Paradigm

ReCo reformulates instructional video editing as an in-context generation task: given a source video and a desired edit expressed in natural language, the source and target videos are concatenated width-wise and fed into a video denoising diffusion transformer (DiT). The source video serves as an explicit conditional branch in the model, facilitating strong token interaction and temporal coherence during the joint denoising process.

Region-Based Constraints

ReCo’s key innovation is the integration of region-wise regularization in both the latent and attention spaces:

• Latent Regularization: Amplifies the latent discrepancy between source and target videos in the editing region while minimizing discrepancies elsewhere. This ensures modifications are localized as intended, with backgrounds preserved.
• Attention Regularization: Suppresses attention weights from the target edit region towards its corresponding source region, while promoting self-attention within the generated content. This mitigates token interference that arises from strong coupling in the source-target concatenation, driving the model to innovate within the edit region while ensuring seamless background integration.

These constraints are implemented as auxiliary losses in the overall multi-task training objective, optimized alongside flow-matching diffusion losses for high-quality video generation.

Figure 2: Overview of ReCo’s architecture, highlighting source-target concatenation and dual regularization via latent and attention space constraints.

Dataset Construction: ReCo-Data

Recent advances in instructional video editing are bottlenecked by the lack of large-scale, high-quality paired data. ReCo-Data overcomes this via a rigorous, automated pipeline that leverages VLLMs and advanced video-generation models to curate 500K instruction-video pairs spanning add, remove, replace, and style tasks. The dataset maintains a significantly higher ratio of usable, high-quality samples ($91.6\%$) than prior alternatives, supporting robust and generalizable training.

Figure 3: ReCo-Data shows superior sample quality and task balance compared to prior datasets.

Figure 4: The multi-stage data construction pipeline, integrating VLLMs, object segmentation, and synthetic video generation for robust data quality.

Experimental Evaluation

Benchmarks and Metrics

To comprehensively assess instructional video editing models, the authors propose a VLLM-based evaluation benchmark covering semantic accuracy, scope precision, content preservation, appearance and scale naturalness, motion characteristics, and traditional video quality metrics. Systematic use of Gemini-2.5-Flash-Thinking ensures rigorous multi-dimensional assessment across 480 test pairs.

Figure 5: System prompts for VLLM-driven video editing evaluation incorporating accuracy, naturalness, and quality criteria.

Quantitative Results

Across four major tasks (add, replace, remove, style), ReCo consistently yields superior scores in total and sub-category metrics (semantic accuracy $S_{EA}$, video naturalness $S_{VN}$, video quality $S_{VQ}$). For instance, in local editing tasks, ReCo achieves $S=8.23$ (Add) and $S=8.74$ (Replace), outperforming leading baselines such as Ditto ($S=7.56$) and Lucy-Edit ($S=6.72$). Notable is ReCo’s simultaneous enhancement of accuracy and fidelity without sacrificing background consistency, even under complex multi-task training regimes.

Qualitative Comparisons

Visual inspection reveals ReCo’s ability to execute both local and global edits with minimal artifacts, precise region localization, and seamless integration of new content. Competitors either fail to preserve spatial-temporal consistency, introduce artifacts, or lose modification intent under ambiguous instructions.

Figure 6: Multi-task editing comparison: ReCo reliably adds, replaces, and stylizes objects as instructed, outperforming baselines.

Figure 7: ReCo exhibits higher precision and fewer artifacts in object removal tasks compared to competing methods.

Ablation Analysis

Variant experiments (removal of latent or attention constraints) demonstrate that both regularization terms are essential: discarding latent constraints sharply degrades region localization and edit precision; removing attention constraints compromises natural object scaling and background coherence.

Figure 8: Editing results among ReCo variants illustrating key contributions from both latent and attention constraints.

Generalization and Emergent Capabilities

Beyond standard edits, ReCo generalizes to abstract and creative instructions, demonstrating emergent compositionality such as synthesizing halos, confetti cascades, and conceptual objects ("idea lightbulbs"). This is traced to effective leveraging of priors from large-scale pretrained video diffusion models.

Figure 9: ReCo synthesizes abstract and creative edits from textual instructions, showcasing generalization capacity.

Implications and Future Prospects

ReCo establishes a scalable, flexible paradigm for instructional video editing, supporting deploying high-performance editors without laborious region annotation or specialized configuration. The region-constraint approach offers strong theoretical guarantees for mitigating token interference, critical in in-context learning for video. Potential extensions include cross-modal editing, more nuanced temporal controls, and further integration with multimodal understanding systems.

On the practical front, ReCo-Data’s openly available dataset marks an inflection point for community progress in instructional video editing, coupling scale and quality unprecedented in the domain.

Conclusion

ReCo effectively advances instructional video editing through a region-constrained in-context generation framework, integrating latent and attention regularization for precise, high-quality edits. Through meticulous data construction and rigorous evaluation, ReCo demonstrates clear superiority over existing approaches, laying a robust foundation for scalable, instruction-driven video post-processing and future research directions in multimodal generative AI.

Reference: (2512.17650)