Generic 3D Diffusion Adapter Using Controlled Multi-View Editing
Authors: name: Hansheng Chen, name: Ruoxi Shi, name: Yulin Liu, name: Bokui Shen, name: Jiayuan Gu, name: Gordon Wetzstein, name: Hao Su, name: Leonidas Guibas

Open-domain 3D object synthesis has been lagging behind image synthesis due
to limited data and higher computational complexity. To bridge this gap, recent
works have investigated multi-view diffusion but often fall short in either 3D
consistency, visual quality, or efficiency. This paper proposes MVEdit, which
functions as a 3D counterpart of SDEdit, employing ancestral sampling to
jointly denoise multi-view images and output high-quality textured meshes.
Built on off-the-shelf 2D diffusion models, MVEdit achieves 3D consistency
through a training-free 3D Adapter, which lifts the 2D views of the last
timestep into a coherent 3D representation, then conditions the 2D views of the
next timestep using rendered views, without uncompromising visual quality. With
an inference time of only 2-5 minutes, this framework achieves better trade-off
between quality and speed than score distillation. MVEdit is highly versatile
and extendable, with a wide range of applications including text/image-to-3D
generation, 3D-to-3D editing, and high-quality texture synthesis. In
particular, evaluations demonstrate state-of-the-art performance in both
image-to-3D and text-guided texture generation tasks. Additionally, we
introduce a method for fine-tuning 2D latent diffusion models on small 3D
datasets with limited resources, enabling fast low-resolution text-to-3D
initialization.