All Outputs (3)

Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models (2023)
Presentation / Conference Contribution
Chang, Z., Findlay, E. J., Zhang, H., & Shum, H. P. (2023). Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - GRAPP (64-74). https://doi.org/10.5220/0011631000003417

Generating realistic motions for digital humans is a core but challenging part of computer animations and games, as human motions are both diverse in content and rich in styles. While the latest deep learning approaches have made significant advancem... Read More about Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models.

Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient (2023)
Presentation / Conference Contribution
Lu, Z., Wang, H., Chang, Z., Yang, G., & Shum, H. P. (2023). Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient. . https://doi.org/10.1109/ICCV51070.2023.00424

Recently, methods for skeleton-based human activity recognition have been shown to be vulnerable to adversarial attacks. However, these attack methods require either the full knowledge of the victim (i.e. white-box attacks), access to training data (... Read More about Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient.

Denoising Diffusion Probabilistic Models for Styled Walking Synthesis (2022)
Presentation / Conference Contribution
Findlay, E., Zhang, H., Chang, Z., & Shum, H. P. (2022). Denoising Diffusion Probabilistic Models for Styled Walking Synthesis. . https://doi.org/10.1145/3561975

Generating realistic motions for digital humans is time-consuming for many graphics applications. Data-driven motion synthesis approaches have seen solid progress in recent years through deep generative models. These results offer high-quality motion... Read More about Denoising Diffusion Probabilistic Models for Styled Walking Synthesis.