AbracADDbra: Touch-Guided Object Addition by Decoupling Placement and Editing Subtasks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914331371765760 |
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| author | Swami, Kunal Chittersu, Raghu Rathore, Yuvraj Irny, Rajeev Doodekula, Shashavali Shukla, Alok |
| author_facet | Swami, Kunal Chittersu, Raghu Rathore, Yuvraj Irny, Rajeev Doodekula, Shashavali Shukla, Alok |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14237 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AbracADDbra: Touch-Guided Object Addition by Decoupling Placement and Editing Subtasks Swami, Kunal Chittersu, Raghu Rathore, Yuvraj Irny, Rajeev Doodekula, Shashavali Shukla, Alok Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | AbracADDbra: Touch-Guided Object Addition by Decoupling Placement and Editing Subtasks |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2602.14237 |