PanoLAM: Large Avatar Model for Gaussian Full-Head Synthesis from One-shot Unposed Image
This enables efficient 3D avatar creation for applications like virtual reality or gaming, though it is incremental as it builds on existing 3D GAN and Gaussian synthesis methods.
The paper tackles the problem of synthesizing a full-head 3D model from a single unposed image, achieving fast reconstruction in a single forward pass without relying on slow GAN inversion or test-time optimization.
We present a feed-forward framework for Gaussian full-head synthesis from a single unposed image. Unlike previous work that relies on time-consuming GAN inversion and test-time optimization, our framework can reconstruct the Gaussian full-head model given a single unposed image in a single forward pass. This enables fast reconstruction and rendering during inference. To mitigate the lack of large-scale 3D head assets, we propose a large-scale synthetic dataset from trained 3D GANs and train our framework using only synthetic data. For efficient high-fidelity generation, we introduce a coarse-to-fine Gaussian head generation pipeline, where sparse points from the FLAME model interact with the image features by transformer blocks for feature extraction and coarse shape reconstruction, which are then densified for high-fidelity reconstruction. To fully leverage the prior knowledge residing in pretrained 3D GANs for effective reconstruction, we propose a dual-branch framework that effectively aggregates the structured spherical triplane feature and unstructured point-based features for more effective Gaussian head reconstruction. Experimental results show the effectiveness of our framework towards existing work. Project page at: https://panolam.github.io/.