We introduce a novel Stylized Motion Diffusion model, dubbed SMooDi, to generate stylized
motion driven by content texts and style motion sequences. Unlike existing methods that …

We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-
work for controlling pre-trained text-to-music diffusion models at inference-time via …

Text-to-motion diffusion models can generate realistic animations from text prompts, but do
not support fine-grained motion editing controls. In this paper, we present a method for using …

Generative models have made significant impacts across various domains, largely due to
their ability to scale during training by increasing data, computational resources, and model …

We propose RoHM an approach for robust 3D human motion reconstruction from monocular
RGB (-D) videos in the presence of noise and occlusions. Most previous approaches either …

Estimating global human motion from moving cameras is challenging due to the
entanglement of human and camera motions. To mitigate the ambiguity, existing methods …

Motion diffusion models and Reinforcement Learning (RL) based control for physics-based
simulations have complementary strengths for human motion generation. The former is …

Text-conditioned human motion generation, which allows for user interaction through natural
language, has become increasingly popular. Existing methods typically generate short …

Text-to-pose generation is challenging due to the complexity of natural language and
human posture semantics. Utilizing large language models (LLMs) for text-to-pose …

Authoring 3D motions is a laborious process that requires manipulating and coordinating
many control handles over time. Neural motion representations learned from large motion …