GCA-3D: Towards Generalized and Consistent Domain Adaptation of 3D Generators

TL;DR

GCA-3D introduces a novel, pipeline-free approach for generalized 3D domain adaptation that leverages multi-modal depth-aware score distillation and hierarchical spatial consistency to improve pose and identity fidelity.

Contribution

It proposes GCA-3D, a new method that enables efficient, generalized, and consistent 3D domain adaptation without complex data generation pipelines.

Findings

Outperforms previous methods in efficiency and generalization.

Achieves better pose accuracy and identity consistency.

Supports both text and one-shot image prompt adaptation.

Abstract

Recently, 3D generative domain adaptation has emerged to adapt the pre-trained generator to other domains without collecting massive datasets and camera pose distributions. Typically, they leverage large-scale pre-trained text-to-image diffusion models to synthesize images for the target domain and then fine-tune the 3D model. However, they suffer from the tedious pipeline of data generation, which inevitably introduces pose bias between the source domain and synthetic dataset. Furthermore, they are not generalized to support one-shot image-guided domain adaptation, which is more challenging due to the more severe pose bias and additional identity bias introduced by the single image reference. To address these issues, we propose GCA-3D, a generalized and consistent 3D domain adaptation method without the intricate pipeline of data generation. Different from previous pipeline methods, we introduce multi-modal depth-aware score distillation sampling loss to efficiently adapt 3D generative models in a non-adversarial manner. This multi-modal loss enables GCA-3D in both text prompt and one-shot image prompt adaptation. Besides, it leverages per-instance depth maps from the volume rendering module to mitigate the overfitting problem and retain the diversity of results. To enhance the pose and identity consistency, we further propose a hierarchical spatial consistency loss to align the spatial structure between the generated images in the source and target domain. Experiments demonstrate that GCA-3D outperforms previous methods in terms of efficiency, generalization, pose accuracy, and identity consistency.