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Found 6 papers, 3 papers with code
CADReN: Contextual Anchor-Driven Relational Network for Controllable Cross-Graphs Node Importance Estimation
CADReN is also proven to match the performance of previous models on single-graph NIE task.
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient
Specifically, we first learn a motion manifold where we define an adversarial loss to compute a new gradient for the attack, named skeleton-motion-informed (SMI) gradient.
On the Design Fundamentals of Diffusion Models: A Survey
Diffusion models are generative models, which gradually add and remove noise to learn the underlying distribution of training data for data generation.
Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models
To achieve high-quality results, we design a multi-task architecture of diffusion model that strategically generates aspects of human motions for local guidance.
3D Reconstruction of Sculptures from Single Images via Unsupervised Domain Adaptation on Implicit Models
Acquiring the virtual equivalent of exhibits, such as sculptures, in virtual reality (VR) museums, can be labour-intensive and sometimes infeasible.
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis
Generating realistic motions for digital humans is time-consuming for many graphics applications.
