  High dynamic range (HDR) imaging aims to retrieve information from multiple
low-dynamic range inputs to generate realistic output. The essence is to
leverage the contextual information, including both dynamic and static
semantics, for better image generation. Existing methods often focus on the
spatial misalignment across input frames caused by the foreground and/or camera
motion. However, there is no research on jointly leveraging the dynamic and
static context in a simultaneous manner. To delve into this problem, we propose
a novel alignment-free network with a Semantics Consistent Transformer (SCTNet)
with both spatial and channel attention modules in the network. The spatial
attention aims to deal with the intra-image correlation to model the dynamic
motion, while the channel attention enables the inter-image intertwining to
enhance the semantic consistency across frames. Aside from this, we introduce a
novel realistic HDR dataset with more variations in foreground objects,
environmental factors, and larger motions. Extensive comparisons on both
conventional datasets and ours validate the effectiveness of our method,
achieving the best trade-off on the performance and the computational cost.
