  The recent success of pre-trained diffusion models unlocks the possibility of
the automatic generation of textures for arbitrary 3D meshes in the wild.
However, these models are trained in the screen space, while converting them to
a multi-view consistent texture image poses a major obstacle to the output
quality. In this paper, we propose a novel method to enforce multi-view
consistency. Our method is based on the observation that latent space in a
pre-trained diffusion model is noised separately for each camera view, making
it difficult to achieve multi-view consistency by directly manipulating the
latent codes. Based on the celebrated Denoising Diffusion Implicit Models
(DDIM) scheme, we propose to use an optimization-based color-fusion to enforce
consistency and indirectly modify the latent codes by gradient
back-propagation. Our method further relaxes the sequential dependency
assumption among the camera views. By evaluating on a series of general 3D
models, we find our simple approach improves consistency and overall quality of
the generated textures as compared to competing state-of-the-arts. Our
implementation is available at: https://github.com/Quantuman134/TexPainter
