Recent deep learning approaches for Diffusion MRI modeling circumvent the requirement of densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs supervised with fully-sampled DWIs. However, they implicitly make unrealistic assumptions of static Q-space sampling during reconstruction. Further, such approaches restrict direct downstream estimations, such as tractography, from arbitrarily sampled DWIs unless we use model fitting methods spherical harmonics, for example. We propose a generative adversarial translation framework for high-quality DW image estimation with arbitrary Q-space sampling given commonly acquired structural images. Our translation network linearly modulates its internal representations conditioned on continuous Q-space information, removing the need for fixed sampling schemes. Moreover, this approach enables the downstream estimation of high-quality microstructural maps from arbitrarily subsampled DWIs, which may be particularly important in cases with sparsely sampled DWIs. Across several recent methodologies, the proposed approach yields improved DWI synthesis accuracy and fidelity with enhanced downstream utility as quantified by the accuracy of scalar microstructure indices estimated from the synthesized images.