{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dream2real-zero-shot-3d-object-rearrangement","title":"Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models","arxiv_id":"2312.04533","date":"2023-12-07","proceeding":null,"authors":["Ivan Kapelyukh","Yifei Ren","Ignacio Alzugaray","Edward Johns"],"abstract":"We introduce Dream2Real, a robotics framework which integrates vision-language models (VLMs) trained on 2D data into a 3D object rearrangement pipeline. This is achieved by the robot autonomously constructing a 3D representation of the scene, where objects can be rearranged virtually and an image of the resulting arrangement rendered. These renders are evaluated by a VLM, so that the arrangement which best satisfies the user instruction is selected and recreated in the real world with pick-and-place. This enables language-conditioned rearrangement to be performed zero-shot, without needing to collect a training dataset of example arrangements. Results on a series of real-world tasks show that this framework is robust to distractors, controllable by language, capable of understanding complex multi-object relations, and readily applicable to both tabletop and 6-DoF rearrangement tasks.","url_abs":"https://arxiv.org/abs/2312.04533v2","url_pdf":"https://arxiv.org/pdf/2312.04533v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"object-rearrangement","task_name":"Object Rearrangement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-rearrangement-on-open6dor-v2","task":"Object Rearrangement","dataset":"Open6DOR V2","model":"Dream2Real","rank_in_archive_order":3,"of":5,"metrics":{"6-DoF":"13.5","pos-level0":"11.0","pos-level1":"17.2","rot-level0":"37.3","rot-level1":"27.6","rot-level2":"26.2"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2312.04533","atlas_url":"https://app.syntology.ai/?focus=2312.04533","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}