Title: InstructMix2Mix: Consistent Sparse-View Editing Through Multi-View Model Personalization
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Image Editing, Multi-view, 3D, Distillation, Diffusion

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.

TL;DR: We enable consistent sparse-view editing by replacing the neural field in an SDS process with a multi-view diffusion model and adapting the key SDS steps accordingly.