Neural Parameterization for Dynamic Human Head Editing
This addresses the challenge of editing detailed dynamic human heads (e.g., facial features, hair) for applications in graphics and animation, though it is incremental as it builds on existing implicit and explicit methods.
The paper tackles the problem of poor editability in implicit radiance functions for dynamic human heads by proposing Neural Parameterization (NeP), a hybrid representation that achieves photo-realistic rendering with fine-grained editing capabilities, showing results with almost the same rendering accuracy as baselines.
Implicit radiance functions emerged as a powerful scene representation for reconstructing and rendering photo-realistic views of a 3D scene. These representations, however, suffer from poor editability. On the other hand, explicit representations such as polygonal meshes allow easy editing but are not as suitable for reconstructing accurate details in dynamic human heads, such as fine facial features, hair, teeth, and eyes. In this work, we present Neural Parameterization (NeP), a hybrid representation that provides the advantages of both implicit and explicit methods. NeP is capable of photo-realistic rendering while allowing fine-grained editing of the scene geometry and appearance. We first disentangle the geometry and appearance by parameterizing the 3D geometry into 2D texture space. We enable geometric editability by introducing an explicit linear deformation blending layer. The deformation is controlled by a set of sparse key points, which can be explicitly and intuitively displaced to edit the geometry. For appearance, we develop a hybrid 2D texture consisting of an explicit texture map for easy editing and implicit view and time-dependent residuals to model temporal and view variations. We compare our method to several reconstruction and editing baselines. The results show that the NeP achieves almost the same level of rendering accuracy while maintaining high editability.