AbracADDbra: 通过解耦放置和编辑子任务实现触控引导的对象添加
AbracADDbra: Touch-Guided Object Addition by Decoupling Placement and Editing Subtasks
AI总结：
AbracADDbra通过解耦放置和编辑子任务，利用触控先验实现高效精准的对象添加，提升了交互式编辑的用户体验和编辑质量。
摘要：
基于指令的对象添加常常受到纯文本提示的模糊性或基于掩码的输入繁琐性的阻碍。为解决这一可用性缺口，我们引入AbracADDbra，一种用户友好的框架，利用直观的触控先验来空间地 grounding 精确放置的简洁指令。我们的高效、解耦架构使用视觉语言变换器进行触控引导的放置，随后由扩散模型联合生成对象和实例掩码，以实现高保真的融合。为了促进标准化评估，我们贡献了Touch2Add基准测试这一交互任务。我们的广泛评估表明，我们的放置模型在随机放置和通用目的VLM基线方面显著优于前者，证实了该框架生成高保真编辑的能力。此外，我们的分析揭示了初始放置准确性与最终编辑质量之间存在强相关性，验证了我们的解耦方法。这项工作因此为更加可访问和高效的创意工具铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
