6 DAILY WACV Friday Poster Presentation Radim’s innovative approach involves simultaneous deblurring of the object and temporal superresolution, a process that generates multiple renderings of the deblurred object in consecutive time steps. Existing methods require at least three consecutive images to reconstruct a fast-moving object due to the need for background estimation. However, these approaches face significant challenges in simultaneously estimating the background, reconstructing the object’s original shape or texture, and determining its trajectory. “We tried a lot of things, but in the end, we didn’t have the proper tool,” he tells us. “We needed a very strong generative model. With the denoising diffusion probabilistic model, we finally got the right tool, and we were able to, at the same time, tackle the background and foreground reconstruction, like the object deblurring and shape recovery.” Radim employed a 3D diffusion model architecture conditioned on a single image. The robustness and ease of use of diffusion models were key to the method’s success. In contrast to Generative Adversarial Networks (GANs), it simplified the process, making it more manageable. Single-Image Deblurring, Trajectory and Shape Recovery of Fast Moving Objects with Denoising Diffusion Probabilistic Models Radim Špetlík is a PhD student at the Czech Technical University under the supervision of Jiri Matas. His paper proposes a novel method to reconstruct a video sequence from a single blurry image of a fastmoving object. He speaks to us ahead of his poster presentation tomorrow.
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