GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing
arxiv(2024)
摘要
We propose GaussCtrl, a text-driven method to edit a 3D scene reconstructed
by the 3D Gaussian Splatting (3DGS).
Our method first renders a collection of images by using the 3DGS and edits
them by using a pre-trained 2D diffusion model (ControlNet) based on the input
prompt, which is then used to optimise the 3D model.
Our key contribution is multi-view consistent editing, which enables editing
all images together instead of iteratively editing one image while updating the
3D model as in previous works.
It leads to faster editing as well as higher visual quality.
This is achieved by the two terms:
(a) depth-conditioned editing that enforces geometric consistency across
multi-view images by leveraging naturally consistent depth maps.
(b) attention-based latent code alignment that unifies the appearance of
edited images by conditioning their editing to several reference views through
self and cross-view attention between images' latent representations.
Experiments demonstrate that our method achieves faster editing and better
visual results than previous state-of-the-art methods.
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