  Cryo-electron microscopy (cryo-EM) allows for the high-resolution
reconstruction of 3D structures of proteins and other biomolecules. Successful
reconstruction of both shape and movement greatly helps understand the
fundamental processes of life. However, it is still challenging to reconstruct
the continuous motions of 3D structures from hundreds of thousands of noisy and
randomly oriented 2D cryo-EM images. Recent advancements use Fourier domain
coordinate-based neural networks to continuously model 3D conformations, yet
they often struggle to capture local flexible regions accurately. We propose
CryoFormer, a new approach for continuous heterogeneous cryo-EM reconstruction.
Our approach leverages an implicit feature volume directly in the real domain
as the 3D representation. We further introduce a novel query-based deformation
transformer decoder to improve the reconstruction quality. Our approach is
capable of refining pre-computed pose estimations and locating flexible
regions. In experiments, our method outperforms current approaches on three
public datasets (1 synthetic and 2 experimental) and a new synthetic dataset of
PEDV spike protein. The code and new synthetic dataset will be released for
better reproducibility of our results. Project page:
https://cryoformer.github.io.
