AbracADDbra: Touch-Guided Object Addition by Decoupling Placement and Editing Subtasks

TL;DR

AbracADDbra introduces a touch-guided framework for precise object addition in images, decoupling placement and editing to improve usability and fidelity, supported by a new benchmark and extensive evaluations.

Contribution

The paper presents a novel touch-guided object addition framework with a decoupled architecture and introduces the Touch2Add benchmark for standardized evaluation.

Findings

Our placement model outperforms random and baseline methods.

High initial placement accuracy correlates with better final edits.

Framework enables high-fidelity, user-friendly image editing.

Abstract

Instruction-based object addition is often hindered by the ambiguity of text-only prompts or the tedious nature of mask-based inputs. To address this usability gap, we introduce AbracADDbra, a user-friendly framework that leverages intuitive touch priors to spatially ground succinct instructions for precise placement. Our efficient, decoupled architecture uses a vision-language transformer for touch-guided placement, followed by a diffusion model that jointly generates the object and an instance mask for high-fidelity blending. To facilitate standardized evaluation, we contribute the Touch2Add benchmark for this interactive task. Our extensive evaluations, where our placement model significantly outperforms both random placement and general-purpose VLM baselines, confirm the framework's ability to produce high-fidelity edits. Furthermore, our analysis reveals a strong correlation between initial placement accuracy and final edit quality, validating our decoupled approach. This work thus paves the way for more accessible and efficient creative tools.