  In this paper, we delve into the intricate dynamics of Fine-Grained
Sketch-Based Image Retrieval (FG-SBIR) by addressing a critical yet overlooked
aspect -- the choice of viewpoint during sketch creation. Unlike photo systems
that seamlessly handle diverse views through extensive datasets, sketch
systems, with limited data collected from fixed perspectives, face challenges.
Our pilot study, employing a pre-trained FG-SBIR model, highlights the system's
struggle when query-sketches differ in viewpoint from target instances.
Interestingly, a questionnaire however shows users desire autonomy, with a
significant percentage favouring view-specific retrieval. To reconcile this, we
advocate for a view-aware system, seamlessly accommodating both view-agnostic
and view-specific tasks. Overcoming dataset limitations, our first contribution
leverages multi-view 2D projections of 3D objects, instilling cross-modal view
awareness. The second contribution introduces a customisable cross-modal
feature through disentanglement, allowing effortless mode switching. Extensive
experiments on standard datasets validate the effectiveness of our method.
