arXiv:2511.14899v1 Announce Type: new 
Abstract: We address the task of multi-view image editing from sparse input views, where the inputs can be seen as a mix of images capturing the scene from different viewpoints. The goal is to modify the scene according to a textual instruction while preserving consistency across all views. Existing methods, based on per-scene neural fields or temporal attention mechanisms, struggle in this setting, often producing artifacts and incoherent edits. We propose InstructMix2Mix (I-Mix2Mix), a framework that distills the editing capabilities of a 2D diffusion model into a pretrained multi-view diffusion model, leveraging its data-driven 3D prior for cross-view consistency. A key contribution is replacing the conventional neural field consolidator in Score Distillation Sampling (SDS) with a multi-view diffusion student, which requires novel adaptations: incremental student updates across timesteps, a specialized teacher noise scheduler to prevent degeneration, and an attention modification that enhances cross-view coherence without additional cost. Experiments demonstrate that I-Mix2Mix significantly improves multi-view consistency while maintaining high per-frame edit quality.

يقدم المقال InstructMix2Mix (I-Mix2Mix)، وهو إطار مصمم لتحرير الصور متعددة المناظر من مدخلات نادرة. يهدف إلى تعديل المشاهد وفقًا لتعليمات نصية مع الحفاظ على التناسق عبر وجهات نظر مختلفة. غالبًا ما تنتج الطرق الحالية عيوبًا وتعديلات غير متسقة، لكن I-Mix2Mix يستفيد من نموذج انتشار متعدد المناظر مدرب مسبقًا لتعزيز التناسق عبر المناظر، مما يعالج هذه التحديات بشكل فعال.

El artículo presenta InstructMix2Mix (I-Mix2Mix), un marco diseñado para la edición de imágenes desde vistas de entrada escasas. Su objetivo es modificar escenas según instrucciones textuales mientras se asegura la consistencia entre diferentes puntos de vista. Los métodos existentes a menudo producen artefactos y ediciones incoherentes, pero I-Mix2Mix aprovecha un modelo de difusión multi-vista preentrenado para mejorar la consistencia entre vistas, abordando estos desafíos de manera efectiva.

L'article présente InstructMix2Mix (I-Mix2Mix), un cadre conçu pour l'édition d'images multi-vues à partir de vues d'entrée rares. Il vise à modifier des scènes en fonction d'instructions textuelles tout en garantissant la cohérence entre différents points de vue. Les méthodes existantes produisent souvent des artefacts et des modifications incohérentes, mais I-Mix2Mix exploite un modèle de diffusion multi-vues pré-entraîné pour améliorer la cohérence entre les vues, répondant ainsi efficacement à ces défis.

The article presents InstructMix2Mix (I-Mix2Mix), a framework designed for multi-view image editing from sparse input views. It aims to modify scenes based on textual instructions while ensuring consistency across various viewpoints. Existing methods often produce artifacts and incoherent edits, but I-Mix2Mix leverages a pretrained multi-view diffusion model to enhance cross-view consistency, addressing these challenges effectively.

InstructMix2Mix: Consistent Sparse-View Editing Through Multi-View Model Personalization

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InstructMix2Mix: Consistent Sparse-View Editing Through Multi-View Model Personalization

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