  We introduce EscherNet, a multi-view conditioned diffusion model for view
synthesis. EscherNet learns implicit and generative 3D representations coupled
with a specialised camera positional encoding, allowing precise and continuous
relative control of the camera transformation between an arbitrary number of
reference and target views. EscherNet offers exceptional generality,
flexibility, and scalability in view synthesis -- it can generate more than 100
consistent target views simultaneously on a single consumer-grade GPU, despite
being trained with a fixed number of 3 reference views to 3 target views. As a
result, EscherNet not only addresses zero-shot novel view synthesis, but also
naturally unifies single- and multi-image 3D reconstruction, combining these
diverse tasks into a single, cohesive framework. Our extensive experiments
demonstrate that EscherNet achieves state-of-the-art performance in multiple
benchmarks, even when compared to methods specifically tailored for each
individual problem. This remarkable versatility opens up new directions for
designing scalable neural architectures for 3D vision. Project page:
https://kxhit.github.io/EscherNet.
