This innovative work analyses a problem common in many fields, not just motion but image and video generation and anything related to generation with diffusion models. It exploits the denoising dynamics of diffusion models to guarantee particular conditions in downstream applications. “In deep learning and computer vision, the aim is to make models learn effectively from the data provided,” German explains. “This research is intriguing because it introduces specific inductive biases into the network. These biases help the network improve at certain tasks. In this case, generating smooth transitions between actions without being explicitly trained on those transitions. This approach is important for researchers as it enhances the network’s ability to perform desired tasks efficiently.” Indeed, the team is eager to test its methods in other scenarios beyond motion generation, where perhaps the constraints are not the same as in the motion domain but need further exploration. “This work solves a specific problem very effectively and simply,” Cristina asserts. “That’s very important. Trying not to complicate things and using this end-to-end method, I believe it can be a very good contribution to the field.” To learn more about the team’s work, visit Poster Session 1 & Exhibit Hall (Arch 4A-E) from 10:30 to 12:00 [Poster 28]. 22 DAILY CVPR Wednesday Poster Presentation
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