  Video grounding aims to localize the target moment in an untrimmed video
corresponding to a given sentence query. Existing methods typically select the
best prediction from a set of predefined proposals or directly regress the
target span in a single-shot manner, resulting in the absence of a systematical
prediction refinement process. In this paper, we propose DiffusionVG, a novel
framework with diffusion models that formulates video grounding as a
conditional generation task, where the target span is generated from Gaussian
noise inputs and interatively refined in the reverse diffusion process. During
training, DiffusionVG progressively adds noise to the target span with a fixed
forward diffusion process and learns to recover the target span in the reverse
diffusion process. In inference, DiffusionVG can generate the target span from
Gaussian noise inputs by the learned reverse diffusion process conditioned on
the video-sentence representations. Without bells and whistles, our DiffusionVG
demonstrates superior performance compared to existing well-crafted models on
mainstream Charades-STA, ActivityNet Captions and TACoS benchmarks.
