  Event cameras harness advantages such as low latency, high temporal
resolution, and high dynamic range (HDR), compared to standard cameras. Due to
the distinct imaging paradigm shift, a dominant line of research focuses on
event-to-video (E2V) reconstruction to bridge event-based and standard computer
vision. However, this task remains challenging due to its inherently ill-posed
nature: event cameras only detect the edge and motion information locally.
Consequently, the reconstructed videos are often plagued by artifacts and
regional blur, primarily caused by the ambiguous semantics of event data. In
this paper, we find language naturally conveys abundant semantic information,
rendering it stunningly superior in ensuring semantic consistency for E2V
reconstruction. Accordingly, we propose a novel framework, called LaSe-E2V,
that can achieve semantic-aware high-quality E2V reconstruction from a
language-guided perspective, buttressed by the text-conditional diffusion
models. However, due to diffusion models' inherent diversity and randomness, it
is hardly possible to directly apply them to achieve spatial and temporal
consistency for E2V reconstruction. Thus, we first propose an Event-guided
Spatiotemporal Attention (ESA) module to condition the event data to the
denoising pipeline effectively. We then introduce an event-aware mask loss to
ensure temporal coherence and a noise initialization strategy to enhance
spatial consistency. Given the absence of event-text-video paired data, we
aggregate existing E2V datasets and generate textual descriptions using the
tagging models for training and evaluation. Extensive experiments on three
datasets covering diverse challenging scenarios (e.g., fast motion, low light)
demonstrate the superiority of our method.
