Title: Unified Approach for 3D-Aware Image Synthesis with SSDNeRF
Introduction:
SSDNeRF is a unified approach for 3D-aware image synthesis that tackles the challenges of comprehensive model development. The goal is to generate scenes and synthesize novel views from images using a generalizable prior of neural radiance fields (NeRF) learned from multi-view images of various objects. This paper presents a new training paradigm that optimizes both a NeRF auto-decoder and a latent diffusion model in a single stage, enabling simultaneous 3D reconstruction and prior learning, even with limited views.
Key Features of SSDNeRF:
1. Single-Stage Training Paradigm:
Traditionally, two-stage approaches relied on pretrained NeRFs as real data for training diffusion models. However, SSDNeRF introduces a novel single-stage training paradigm. By optimizing the NeRF auto-decoder and latent diffusion model together, it achieves more efficient 3D reconstruction and prior learning, even when dealing with sparsely available views.
2. Simultaneous 3D Reconstruction and Prior Learning:
With SSDNeRF, 3D reconstruction and prior learning occur simultaneously. This allows for the reconstruction of 3D representations and the learning of the prior distribution from multi-view images. The approach handles the challenge of generating unconditional content through direct sampling of the diffusion prior at test time. Moreover, it can incorporate observations of unseen objects for NeRF reconstruction, enhancing the model’s versatility.
Results and Comparisons:
SSDNeRF’s performance is robust and compares favorably to state-of-the-art task-specific methods in both unconditional generation and single/sparse-view 3D reconstruction. The approach demonstrates remarkable results, either matching or surpassing the capabilities of leading existing methods.
Conclusion:
SSDNeRF offers a unified solution for 3D-aware image synthesis, significantly advancing the field. Its single-stage training paradigm, enabling simultaneous 3D reconstruction and prior learning, proves to be a valuable contribution. By generating scenes and synthesizing novel views, SSDNeRF delivers robust results, demonstrating its superiority in unconditional generation and single/sparse-view 3D reconstruction.
Keywords: 3D-aware image synthesis, SSDNeRF, neural radiance fields, multi-view images, single-stage training, prior learning, 3D reconstruction, unconditional generation, novel view synthesis.