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

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 …

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 …

In this paper, we introduce a novel text-to-avatar generation method that separately
generates the human body and the clothes and allows high-quality animation on the …

Conventional text-to-motion generation methods are usually trained on limited text-motion
pairs, making them hard to generalize to open-vocabulary scenarios. Some works use the …

Reconstructing 3D hand-face interactions with deformations from a single image is a
challenging yet crucial task with broad applications in AR, VR, and gaming. The challenges …

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

We propose a novel framework COLLAGE for generating collaborative agent-object-agent
interactions by leveraging large language models (LLMs) and hierarchical motion-specific …

The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes
the refereed proceedings of the 18th European Conference on Computer Vision, ECCV …

Diverse human motion prediction (HMP) is a fundamental application in computer vision that
has recently attracted considerable interest. Prior methods primarily focus on the stochastic …