35 Mathis Petrovich Computer Vision News Computer Vision News In his third work, he addresses the adjacent task of text-to-3D human motion retrieval, where the goal is to search in a motion collection by querying via text. He introduces a simple yet effective approach, named TMR, building on his earlier model TEMOS, by integrating a contrastive loss to enhance the structure of the cross-modal latent space. His findings emphasize the importance of retaining the motion generation loss in conjunction with contrastive training for improved results. He establishes a new evaluation benchmark and conduct analyses on several protocols. In his fourth work, he introduces a new problem termed as “multi-track timeline control” for text-driven 3D human motion synthesis. Instead of a single textual prompt, users can organize multiple prompts in temporal intervals that may overlap. He introduces STMC, a test-time denoising method that can be integrated with any pre-trained motion diffusion model. His evaluations demonstrate that his method generates motions that closely match the semantic and temporal aspects of the input timelines. Mathis has also played a major role in other projects: generating human motions with spatial compositions or temporal compositions. For more information, see his website.
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