Why Not Rethink Talking Portrait Generation through Dynamic Evolution? Motion Interaction Representation Makes It Possible

In recent years, 3DGS-based methods have made significant progress in portrait reconstruction and generation. However, existing methods still struggle to capture the interactions among multiple motion factors over time, often resulting in incoherent fine-grained dynamics and insufficient coordination across different motion components. The fundamental reason is that 3DGS adopts an explicit parameterized representation based on discrete Gaussian primitives, which primarily describes spatial states but lacks an effective mechanism for characterizing the interactions among motion factors and their state evolution. We argue that the key to dynamic portrait generation lies in learning an interaction-oriented state evolution mechanism that enables the model to predict future changes based on the current dynamic state, rather than relying on local observations for frame-by-frame fitting. To this end, we propose a talking portrait generation framework based on motion interaction representation, which establishes relationships among dynamic factors from two aspects: static-dynamic factor fusion (S-DF) and rigid-flexible factor alignment (R-FA). S-DF establishes the static-dynamic interaction representation to enhance the expression of fine-grained dynamic evolution, while R-FA develops the rigid-flexible interaction representation to enable coordinated dynamic changes across different regions. Extensive experiments on talking portrait generation demonstrate that our method effectively captures the interactions among motion factors, resulting in portrait videos with higher fidelity in fine-grained details and more natural motion coordination.