Reconstructing sharp 3D representations from blurry multi-view images are
long-standing problem in computer vision. Recent works attempt to enhance
high-quality novel view synthesis from the motion blur by leveraging
event-based cameras, benefiting from high dynamic range and microsecond
temporal resolution. However, they often reach sub-optimal visual quality in
either restoring inaccurate color or losing fine-grained details. In this
paper, we present DiET-GS, a diffusion prior and event stream-assisted motion
deblurring 3DGS. Our framework effectively leverages both blur-free event
streams and diffusion prior in a two-stage training strategy. Specifically, we
introduce the novel framework to constraint 3DGS with event double integral,
achieving both accurate color and well-defined details. Additionally, we
propose a simple technique to leverage diffusion prior to further enhance the
edge details. Qualitative and quantitative results on both synthetic and
real-world data demonstrate that our DiET-GS is capable of producing
significantly better quality of novel views compared to the existing baselines.
Our project page is https://diet-gs.github.io
