ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation
2026-07-09 • Graphics
GraphicsComputer Vision and Pattern RecognitionMachine LearningRobotics
AI summaryⓘ
The authors created ARDY, a system that generates realistic 3D human motions quickly enough for interactive use, like games or robots. It combines detailed control from text instructions and body poses with fast processing by using a mix of clear movement cues and hidden body information. Their method uses a special transformer model that learns from lots of motion data to follow long and flexible movement goals. Tests show ARDY produces high-quality motions that respect the given constraints, and they demonstrate it working in real-time with hands-on controls. This approach aims to balance speed, control, and realism in motion generation.
3D human motion generationreal-time synthesiskinematic constraintsautoregressive transformerlatent body embeddingmotion capture datasetHumanML3D benchmarkinteractive animationmotion controltext-conditioned generation
Authors
Kaifeng Zhao, Mathis Petrovich, Haotian Zhang, Tingwu Wang, Siyu Tang, Davis Rempe
Abstract
Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics. While recent offline motion generation approaches offer precise control via text and kinematic constraints, they lack the inference speed required for interactive settings. Conversely, existing online methods enable real-time synthesis but often sacrifice controllability or struggle with complex text semantics and long-horizon goals due to limited context windows. In this work, we introduce ARDY, a streaming generation framework that bridges this gap by enabling high-fidelity motion generation controllable via online text prompts and flexible kinematic constraints. ARDY employs a hybrid representation that combines explicit root features with a latent body embedding, balancing precise trajectory control with efficient generative learning. We propose a two-stage autoregressive transformer denoiser that features variable history context and supports conditioning on flexible, long-horizon kinematic constraints. By training on a large-scale motion capture dataset and being directly conditioned on text labels and kinematic constraints sampled from ground truth poses, ARDY natively learns controllable generation that supports online prompting and flexible long-horizon goals. Extensive evaluations on the HumanML3D benchmark and the large-scale, high-fidelity Bones Rigplay dataset demonstrate ARDY's high motion quality and constraint adherence, validating the efficacy of our key architectural decisions. Finally, we demonstrate the method's practical versatility through an interactive demo featuring dynamic text control, diverse keyframe pose constraints, path following, and interactive locomotion control via mouse and keyboard. Supplementary video results, code, and model releases can be found at https://research.nvidia.com/labs/sil/projects/ardy/.