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Diffusion-based generative models have recently emerged as powerful solutionsfor high-quality synthesis in multiple domains. Leveraging the bidirectionalMarkov chains, diffusion probabilistic models generate samples by inferring thereversed Markov chain based on the learned distribution mapping at the forwarddiffusion process. In this work, we propose Modiff, a conditional paradigm thatbenefits from the denoising diffusion probabilistic model (DDPM) to tackle theproblem of realistic and diverse action-conditioned 3D skeleton-based motiongeneration. We are a pioneering attempt that uses DDPM to synthesize a variablenumber of motion sequences conditioned on a categorical action. We evaluate ourapproach on the large-scale NTU RGB+D dataset and show improvements overstate-of-the-art motion generation methods.

Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models