3D Gaussian Head Avatars with Expressive Dynamic Appearances by Compact Tensorial Representations
This work solves the problem of efficient and expressive 3D head avatar creation for applications like virtual reality or gaming, but it is incremental as it builds on existing 3D Gaussian and 3DMM approaches.
The paper tackles the problem of constructing high-quality 3D head avatars by addressing limitations in capturing dynamic textures and reducing runtime and storage overhead, resulting in a method that enables accurate face dynamic details with real-time rendering and significantly reduced storage costs.
Recent studies have combined 3D Gaussian and 3D Morphable Models (3DMM) to construct high-quality 3D head avatars. In this line of research, existing methods either fail to capture the dynamic textures or incur significant overhead in terms of runtime speed or storage space. To this end, we propose a novel method that addresses all the aforementioned demands. In specific, we introduce an expressive and compact representation that encodes texture-related attributes of the 3D Gaussians in the tensorial format. We store appearance of neutral expression in static tri-planes, and represents dynamic texture details for different expressions using lightweight 1D feature lines, which are then decoded into opacity offset relative to the neutral face. We further propose adaptive truncated opacity penalty and class-balanced sampling to improve generalization across different expressions. Experiments show this design enables accurate face dynamic details capturing while maintains real-time rendering and significantly reduces storage costs, thus broadening the applicability to more scenarios.