GuideFlow3D: Optimization-Guided Flow For Appearance Transfer
Sayan Deb Sarkar, Sinisa Stekovic, Vincent Lepetit, Iro Armeni
Transferring appearance to 3D assets using different representations of the appearance object--such as images or text--has garnered interest due to its wide range of applications in industries like gaming, augmented reality, and digital content creation. However, state-of-the-art methods still fail when the geometry between the input and appearance objects is significantly different. A straightforward approach is to directly apply a 3D generative model, but we show that this ultimately fails to produce appealing results. Instead, we propose a principled approach inspired by universal guidance. Given a pretrained rectified flow model conditioned on image or text, our training-free method interacts with the sampling process by periodically adding guidance. This guidance can be modeled as a differentiable loss function, and we experiment with two different types of guidance including part-aware losses for appearance and self-similarity. Our experiments show that our approach successfully transfers texture and geometric details to the input 3D asset. We outperform baselines both qualitatively and quantitatively. Traditional metrics are not suitable for evaluating the task due to inability of focusing on local details and comparing dissimilar inputs, in absence of ground truth data. We thus evaluate appearance transfer quality with a GPT-based system objectively ranking outputs, ensuring robust and human-like assessment. Beyond showcased scenarios, our method is general and could be extended to different types of diffusion models and guidance functions.