{"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/3d-adapter-geometry-consistent-multi-view","title":"3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation","arxiv_id":"2410.18974","date":"2024-10-24","proceeding":null,"authors":["Hansheng Chen","Bokui Shen","Yulin Liu","Ruoxi Shi","Linqi Zhou","Connor Z. Lin","Jiayuan Gu","Hao Su","Gordon Wetzstein","Leonidas Guibas"],"abstract":"Multi-view image diffusion models have significantly advanced open-domain 3D object generation. However, most existing models rely on 2D network architectures that lack inherent 3D biases, resulting in compromised geometric consistency. To address this challenge, we introduce 3D-Adapter, a plug-in module designed to infuse 3D geometry awareness into pretrained image diffusion models. Central to our approach is the idea of 3D feedback augmentation: for each denoising step in the sampling loop, 3D-Adapter decodes intermediate multi-view features into a coherent 3D representation, then re-encodes the rendered RGBD views to augment the pretrained base model through feature addition. We study two variants of 3D-Adapter: a fast feed-forward version based on Gaussian splatting and a versatile training-free version utilizing neural fields and meshes. Our extensive experiments demonstrate that 3D-Adapter not only greatly enhances the geometry quality of text-to-multi-view models such as Instant3D and Zero123++, but also enables high-quality 3D generation using the plain text-to-image Stable Diffusion. Furthermore, we showcase the broad application potential of 3D-Adapter by presenting high quality results in text-to-3D, image-to-3D, text-to-texture, and text-to-avatar tasks.","url_abs":"https://arxiv.org/abs/2410.18974v1","url_pdf":"https://arxiv.org/pdf/2410.18974v1.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":"3d-adapter-geometry-consistent-multi-view","repo_url":"https://github.com/Lakonik/MVEdit","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-generation","task_name":"3D Generation"},{"task_slug":"3d-geometry","task_name":"3D geometry"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-to-3d","task_name":"Image to 3D"},{"task_slug":"text-to-3d","task_name":"Text to 3D"}],"methods":[{"method_slug":"base","method_name":"BASE"},{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.18974","atlas_url":"https://app.syntology.ai/?focus=2410.18974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.18974"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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