This work introduces MotionLCM, extending controllable motion generation to a real-time
level. Existing methods for spatial-temporal control in text-conditioned motion generation …

Text-conditioned human motion generation has experienced significant advancements with
diffusion models trained on extensive motion capture data and corresponding textual …

Abstract We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality
human motion generation. Current state-of-the-art generative diffusion models have …

Abstract We propose Diffusion Noise Optimization (DNO) a new method that effectively
leverages existing motion diffusion models as motion priors for a wide range of motion …

This work targets a novel text-driven whole-body motion generation task, which takes a
given textual description as input and aims at generating high-quality, diverse, and coherent …

Controllable human motion synthesis is essential for applications in AR/VR, gaming and
embodied AI. Existing methods often focus solely on either language or full trajectory control …

We address the problem of generating realistic 3D motions of humans interacting with
objects in a scene. Our key idea is to create a neural interaction field attached to a specific …

We present TeSMo, a text-controlled scene-aware motion generation method based on
denoising diffusion models. Previous text-to-motion methods focus on characters in isolation …

Human motion generation, a cornerstone technique in animation and video production, has
widespread applications in various tasks like text-to-motion and music-to-dance. Previous …

Generating human motions from textual descriptions has gained growing research interest
due to its wide range of applications. However only a few works consider human-scene …