Generic 3D Diffusion Adapter Using Controlled Multi-View Editing
This addresses the lag in 3D synthesis compared to image synthesis for researchers and practitioners in computer graphics and AI, offering a versatile and efficient solution, though it builds on existing 2D diffusion models.
The paper tackles the problem of open-domain 3D object synthesis by proposing MVEdit, a method that uses a training-free 3D Adapter to achieve 3D consistency and high-quality textured meshes from multi-view images, with inference times of 2-5 minutes and state-of-the-art performance in tasks like image-to-3D generation.
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.