Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 5524-5535· 0 citations· 6 references
Abstract
Backbone training-free video editing built on pre-trained text-to-image (T2I) diffusion models enables lightweight, prompt-driven edits without additional finetuning. A critical yet often overlooked factor is cross-frame latent selection during DDIM inversion, which largely determines spatiotemporal coherence in the subsequent denoising process. Existing pipelines typically rely on static, heuristic keyframe policies and temperature-softmax responsibilities, yielding unscalability i.e., numerical sensitivity and scale bias, that degrades generalization across diverse scenes. In this paper, we propose VIVID (Variational Inference for Video editing with Image Diffusion), an uncertainty-aware variational latent anchoring module that dynamically selects informative frames and compresses cross-frame latents into a compact set of semantic anchors. VIVID learns stable assignments via a variational objective with contrastive alignment and prior regularization, producing anchors that preserve spatial details while enforcing temporal continuity, and can be plugged into existing backbone training-free T2I-based video editing frameworks as a drop-in replacement for heuristic selection. Extensive experiments on standard benchmarks and in-the-wild videos demonstrate that VIVID achieves state-of-the-art inversion fidelity, editing quality, and temporal consistency, while reducing memory and runtime compared with prior backbone training-freebaselines. Code is released in: https://github.com/amasawa/VIVID.
OSVE is presented, the first framework to successfully adapt one-step Text-to-Image (T2I) models for high-quality video editing, addressing the core challenges of inversion, editability, and temporal consistency.
AnchorSteer is proposed, a training-free framework that exerts fine-grained control over both initialization and denoising trajectory that consistently outperforms existing baselines in text--image alignment while preserving high visual quality.
Xinyi Wang, Yuyang Huang, Yalin Su et al.· arXiv.org· 0 citations
Video-text models adapted from image-text architectures (e.g., CLIP) frequently exhibit temporal blindness, the inability to perceive fundamental cues like order, direction, and motion dynamics. Standard datasets mask this limitation by enabling models to exploit static spatial shortcuts. To systematically evaluate this, we introduce XTE-Bench, a diagnostic probe revealing that even large-scale video-language models struggle with basic temporal reasoning, indicating that parameter scaling alone is insufficient to resolve this flaw. To address this, we propose Cross-Modal Temporal Edits (XTE), a self-supervised framework that injects precise temporal supervision. By performing synchronized video-text transformations, XTE generates hard temporal negatives without manual annotation. We instantiate this with ViTAL-X, a lightweight model that equips frozen image-text backbones with temporal awareness while preserving their foundational spatial knowledge. Across six temporal benchmarks, ViTAL-X achieves state-of-the-art performance. Utilizing only 0.4B parameters and 1M training clips, ViTAL-X outperforms 7B-parameter models and surpasses baselines trained on 600x more data. These results demonstrate that targeted, high-quality temporal alignment provides a highly efficient alternative to pure scaling.
V. SethuramanT., Savya Khosla, O. Susladkar et al.· 0 citations
KATok (Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation), a transformer-based VAE that incorporates an adaptive token selector which is jointly learned with latent tokens that achieves strong reconstruction and generation quality at a state-of-the-art compression ratio.
Yeonkyeong Lee, Hyun-Young Go, Jongmin Kim et al.· 0 citations
Instruction-based general video editing seeks to unify diverse editing operations within a single, intuitive interface. Existing approaches often rely on resource-intensive conditioning, using either heavyweight branches or costly source concatenation. Is there any efficient way to model editing intent? Thus, we introduce GRNEdit, a lightweight two-stage framework. GRN inspires our approach by encoding visual semantics through combinations of bits. Through task-specific fine-tuning, we take this representation further and recast editing semantics as local retain-or-flip decisions over individual bits. Source information is consequently modeled as coordinate-wise evidence supporting the observed binary states, while the GRN backbone remains responsible for resolving their global composition into coherent generative semantics. In Stage I, a compact encoder translates discrete source codes into continuous evidence signals, which GRN assimilates throughout binary refinement. Inspired by null-prompt training for classifier-free guidance, we further assign the null condition an editing-specific meaning: an empty instruction denotes no edit and is supervised through source reconstruction. This identity pathway not only implicitly strengthens evidence utilization and content preservation in Stage I, but also produces a source-preserving state in the same representation space as the edited state. Stage II can therefore directly compare each edited state with its source-preserving counterpart and use their discrepancy to revise unresolved target-bit decisions. Trained on only 0.6M pairs with less than 3\% conditioning parameters, GRNEdit-2B and GRNEdit-8B achieve scores of 4.03 and 4.18 on OpenVE-Bench. The 2B model outperforms multiple 14B open-source editors, while the 8B model performs on par with leading open-source editors.
Recent Artificial Intelligence (AI)-based video editing has enabled users to edit videos through simple text prompts, significantly simplifying the editing process. However, recent zero-shot video editing techniques focus on global or single-object edits, which can lead to unintended changes in other parts of the video. When editing multiple objects only within their localized regions, existing methods face challenges, such as unfaithful editing, editing leakage, and lack of suitable evaluation datasets and metrics. To overcome these limitations, we propose Probability Redistribution for Instance-aware Multi-object Video Editing (PRIMEdit). PRIMEdit is a zero-shot framework that introduces two key modules: (i) Instance-centric Probability Redistribution (IPR) to ensure precise localization and faithful editing and (ii) Disentangled Multi-instance Sampling (DMS) to prevent editing leakage. Additionally, we present the MIVE dataset for multi-instance video editing, including its subsets categorized by instance counts and sizes for controlled analysis across different levels of scene complexity. We also introduce the Cross-Instance Accuracy (CIA) Score that is firstly proposed to evaluate the editing leakage in multi-instance video editing tasks, which exhibits strong correlation with human evaluation as measured by Spearman's $\rho = 0.86$. Our extensive qualitative, quantitative, and user study evaluations demonstrate that PRIMEdit significantly outperforms recent state-of-the-art methods in terms of editing faithfulness, accuracy, and leakage prevention, setting a new benchmark for multi-instance video editing.
Samuel Teodoro, Agus Gunawan, S. Kim et al.· IEEE Transactions on Pattern...· 1 citation
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