{"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/dynamic-2d-gaussians-geometrically-accurate","title":"Dynamic 2D Gaussians: Geometrically accurate radiance fields for dynamic objects","arxiv_id":"2409.14072","date":"2024-09-21","proceeding":null,"authors":["Shuai Zhang","Guanjun Wu","Xinggang Wang","Bin Feng","Wenyu Liu"],"abstract":"Reconstructing objects and extracting high-quality surfaces play a vital role in the real world. Current 4D representations show the ability to render high-quality novel views for dynamic objects but cannot reconstruct high-quality meshes due to their implicit or geometrically inaccurate representations. In this paper, we propose a novel representation that can reconstruct accurate meshes from sparse image input, named Dynamic 2D Gaussians (D-2DGS). We adopt 2D Gaussians for basic geometry representation and use sparse-controlled points to capture 2D Gaussian's deformation. By extracting the object mask from the rendered high-quality image and masking the rendered depth map, a high-quality dynamic mesh sequence of the object can be extracted. Experiments demonstrate that our D-2DGS is outstanding in reconstructing high-quality meshes from sparse input. More demos and code are available at https://github.com/hustvl/Dynamic-2DGS.","url_abs":"https://arxiv.org/abs/2409.14072v1","url_pdf":"https://arxiv.org/pdf/2409.14072v1.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":"dynamic-2d-gaussians-geometrically-accurate","repo_url":"https://github.com/hustvl/Dynamic-2DGS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2409.14072","atlas_url":"https://app.syntology.ai/?focus=2409.14072","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}