We propose a new framework to create high-quality character head morphable models from text, combining static text-to-3D generation with video diffusion. Bridging the gap between these two methods is challenging: text-to-3D models produce detailed static geometry but cannot synthesize motion, while video diffusion models generate motion but face consistency issues like varying colors, varying viewpoints, or geometric distortion. Our solution uses deformable 3D Gaussian splatting to align static 3D models with video diffusion outputs, enabling the creation of a set of diverse, expressive motions with greater accuracy. By incorporating static geometry as a constraint and using a view-dependent deformation MLP, we reduce video artifacts and produce coherent, consistent results. This approach allows us to build a 3D morphable model that can generate new, realistic expressions. Compared to existing 4D generation techniques, our method achieves superior results and creates expressive character head models that can be animated.