{"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/4d-dress-a-4d-dataset-of-real-world-human","title":"4D-DRESS: A 4D Dataset of Real-world Human Clothing with Semantic Annotations","arxiv_id":"2404.18630","date":"2024-04-29","proceeding":"CVPR 2024 1","authors":["Wenbo Wang","Hsuan-I Ho","Chen Guo","Boxiang Rong","Artur Grigorev","Jie Song","Juan Jose Zarate","Otmar Hilliges"],"abstract":"The studies of human clothing for digital avatars have predominantly relied on synthetic datasets. While easy to collect, synthetic data often fall short in realism and fail to capture authentic clothing dynamics. Addressing this gap, we introduce 4D-DRESS, the first real-world 4D dataset advancing human clothing research with its high-quality 4D textured scans and garment meshes. 4D-DRESS captures 64 outfits in 520 human motion sequences, amounting to 78k textured scans. Creating a real-world clothing dataset is challenging, particularly in annotating and segmenting the extensive and complex 4D human scans. To address this, we develop a semi-automatic 4D human parsing pipeline. We efficiently combine a human-in-the-loop process with automation to accurately label 4D scans in diverse garments and body movements. Leveraging precise annotations and high-quality garment meshes, we establish several benchmarks for clothing simulation and reconstruction. 4D-DRESS offers realistic and challenging data that complements synthetic sources, paving the way for advancements in research of lifelike human clothing. Website: https://ait.ethz.ch/4d-dress.","url_abs":"https://arxiv.org/abs/2404.18630v1","url_pdf":"https://arxiv.org/pdf/2404.18630v1.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":[{"paper_slug":"4d-dress-a-4d-dataset-of-real-world-human","repo_url":"https://github.com/eth-ait/4d-dress","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"human-parsing","task_name":"Human Parsing"}],"methods":[],"datasets_introduced":[{"slug":"4d-dress","name":"4D-DRESS","full_name":"A 4D Dataset of Real-world Human Clothing with Semantic Annotations"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2404.18630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.18630"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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