  Light fields (LFs), conducive to comprehensive scene radiance recorded across
angular dimensions, find wide applications in 3D reconstruction, virtual
reality, and computational photography.However, the LF acquisition is
inevitably time-consuming and resource-intensive due to the mainstream
acquisition strategy involving manual capture or laborious software
synthesis.Given such a challenge, we introduce LFdiff, a straightforward yet
effective diffusion-based generative framework tailored for LF synthesis, which
adopts only a single RGB image as input.LFdiff leverages disparity estimated by
a monocular depth estimation network and incorporates two distinctive
components: a novel condition scheme and a noise estimation network tailored
for LF data.Specifically, we design a position-aware warping condition scheme,
enhancing inter-view geometry learning via a robust conditional signal.We then
propose DistgUnet, a disentanglement-based noise estimation network, to harness
comprehensive LF representations.Extensive experiments demonstrate that LFdiff
excels in synthesizing visually pleasing and disparity-controllable light
fields with enhanced generalization capability.Additionally, comprehensive
results affirm the broad applicability of the generated LF data, spanning
applications like LF super-resolution and refocusing.
