We present Diffuse-CLoC, a guided diffusion framework for physics-based look-ahead control that enables intuitive, steerable, and physically realistic motion generation. While existing kinematics motion generation with diffusion models offer intuitive steering capabilities with inference-time conditioning, they often fail to produce physically viable motions. In contrast, recent diffusion-based control policies have shown promise in generating physically realizable motion sequences, but the lack of kinematics prediction limits their steerability. Diffuse-CLoC addresses these challenges through a key insight: modeling the joint distribution of states and actions within a single diffusion model makes action generation steerable by conditioning it on the predicted states. This approach allows us to leverage established conditioning techniques from kinematic motion generation while producing physically realistic motions. As a result, we achieve planning capabilities without the need for a high-level planner. Our method handles a diverse set of unseen long-horizon downstream tasks through a single pre-trained model, including static and dynamic obstacle avoidance, motion in-betweening, and task-space control. Experimental results show that our method significantly outperforms the traditional hierarchical framework of high-level motion diffusion and low-level tracking.
View on arXiv@article{huang2025_2503.11801,
title={ Diffuse-CLoC: Guided Diffusion for Physics-based Character Look-ahead Control },
author={ Xiaoyu Huang and Takara Truong and Yunbo Zhang and Fangzhou Yu and Jean Pierre Sleiman and Jessica Hodgins and Koushil Sreenath and Farbod Farshidian },
journal={arXiv preprint arXiv:2503.11801},
year={ 2025 }
}