Diffusion Time-step Curriculum for One Image to 3D Generation

• URL: http://arxiv.org/abs/2404.04562v3
• Date: Fri, 3 May 2024 01:59:57 GMT
• Title: Diffusion Time-step Curriculum for One Image to 3D Generation
• Authors: Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, Hanwang Zhang,
• Abstract summary: Score distillation sampling(SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a textbfsingle image. We find out the crux is the overlooked indiscriminate treatment of diffusion time-steps during optimization. We propose the Diffusion Time-step Curriculum one-image-to-3D pipeline (DTC123), which involves both the teacher and student models collaborating with the time-step curriculum in a coarse-to-fine manner.
• Score: 91.07638345953016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Score distillation sampling~(SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a \textbf{single} image. It leverages pre-trained 2D diffusion models as teacher to guide the reconstruction of student 3D models. Despite their remarkable success, SDS-based methods often encounter geometric artifacts and texture saturation. We find out the crux is the overlooked indiscriminate treatment of diffusion time-steps during optimization: it unreasonably treats the student-teacher knowledge distillation to be equal at all time-steps and thus entangles coarse-grained and fine-grained modeling. Therefore, we propose the Diffusion Time-step Curriculum one-image-to-3D pipeline (DTC123), which involves both the teacher and student models collaborating with the time-step curriculum in a coarse-to-fine manner. Extensive experiments on NeRF4, RealFusion15, GSO and Level50 benchmark demonstrate that DTC123 can produce multi-view consistent, high-quality, and diverse 3D assets. Codes and more generation demos will be released in https://github.com/yxymessi/DTC123.

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