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Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

FromPapers Read on AI


Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

FromPapers Read on AI

ratings:
Length:
18 minutes
Released:
Oct 29, 2023
Format:
Podcast episode

Description

We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view. To take full advantage of pretrained 2D generative priors, we develop various conditioning and training schemes to minimize the effort of finetuning from off-the-shelf image diffusion models such as Stable Diffusion. Zero123++ excels in producing high-quality, consistent multi-view images from a single image, overcoming common issues like texture degradation and geometric misalignment. Furthermore, we showcase the feasibility of training a ControlNet on Zero123++ for enhanced control over the generation process. The code is available at https://github.com/SUDO-AI-3D/zero123plus.

2023: Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, Hao Su



https://arxiv.org/pdf/2310.15110v1.pdf
Released:
Oct 29, 2023
Format:
Podcast episode

Titles in the series (100)

Keeping you up to date with the latest trends and best performing architectures in this fast evolving field in computer science. Selecting papers by comparative results, citations and influence we educate you on the latest research. Consider supporting us on Patreon.com/PapersRead for feedback and ideas.