3D-Fixup: Advancing Photo Editing with 3D Priors

• URL: http://arxiv.org/abs/2505.10566v1
• Date: Thu, 15 May 2025 17:59:51 GMT
• Title: 3D-Fixup: Advancing Photo Editing with 3D Priors
• Authors: Yen-Chi Cheng, Krishna Kumar Singh, Jae Shin Yoon, Alex Schwing, Liangyan Gui, Matheus Gadelha, Paul Guerrero, Nanxuan Zhao,
• Abstract summary: 3D-Fixup is a new framework for editing 2D images guided by learned 3D priors.<n>We leverage a training-based approach that harnesses the generative power of diffusion models.<n>We show that 3D-Fixup effectively supports complex, identity coherent 3D-aware edits.
• Score: 32.83193513442457
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite significant advances in modeling image priors via diffusion models, 3D-aware image editing remains challenging, in part because the object is only specified via a single image. To tackle this challenge, we propose 3D-Fixup, a new framework for editing 2D images guided by learned 3D priors. The framework supports difficult editing situations such as object translation and 3D rotation. To achieve this, we leverage a training-based approach that harnesses the generative power of diffusion models. As video data naturally encodes real-world physical dynamics, we turn to video data for generating training data pairs, i.e., a source and a target frame. Rather than relying solely on a single trained model to infer transformations between source and target frames, we incorporate 3D guidance from an Image-to-3D model, which bridges this challenging task by explicitly projecting 2D information into 3D space. We design a data generation pipeline to ensure high-quality 3D guidance throughout training. Results show that by integrating these 3D priors, 3D-Fixup effectively supports complex, identity coherent 3D-aware edits, achieving high-quality results and advancing the application of diffusion models in realistic image manipulation. The code is provided at https://3dfixup.github.io/

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