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UniPhys: Unified Planner and Controller with Diffusion for Flexible Physics-Based Character Control
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Generating natural and physically plausible character motion remains challenging, particularly for long-horizon control with diverse guidance signals. While prior work combines high-level diffusion-based motion planners with low-level physics controllers, these systems suffer from domain gaps that degrade motion quality and require task-specific fine-tuning. To tackle this problem, we introduce UniPhys, a diffusion-based behavior cloning framework that unifies motion planning and control into a single model. UniPhys enables flexible, expressive character motion conditioned on multi-modal inputs such as text, trajectories, and goals. To address accumulated prediction errors over long sequences, UniPhys is trained with the Diffusion Forcing paradigm, learning to denoise noisy motion histories and handle discrepancies introduced by the physics simulator. This design allows UniPhys to robustly generate physically plausible, long-horizon motions. Through guided sampling, UniPhys generalizes to a wide range of control signals, including unseen ones, without requiring task-specific fine-tuning. Experiments show that UniPhys outperforms prior methods in motion naturalness, generalization, and robustness across diverse control tasks.
Forward citations
Cited by 6 Pith papers
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ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation
An autoregressive diffusion model with a hybrid explicit-root/latent-body representation generates real-time, controllable 3D human motion from text and spatial constraints.
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BeyondMimic combines compact motion tracking with a unified guided latent diffusion model to master diverse agile behaviors from human demos and solve unseen downstream tasks via test-time classifier guidance.
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DiscoForcing introduces a causal diffusion-forcing model with a hybrid temporal schedule for stable real-time audio-to-motion generation under abrupt audio changes.
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Multi-scale Coarse-to-fine Modeling for Test-time Human Motion Control
MSCoT uses multi-scale hierarchical token prediction, multi-scale guidance, and a token refiner to deliver SOTA text-to-motion control with 48% FID gain, 61% lower error, and 10x faster inference on HumanML3D.
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Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary
Humanoid-LLA converts unconstrained natural language commands into stable whole-body motions for humanoid robots using a unified motion vocabulary and two-stage supervised-plus-reinforcement fine-tuning.
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GPC: Large-Scale Generative Pretraining for Transferable Motor Control
GPC learns a motion vocabulary via Finite Scalar Quantization and end-to-end RL, then trains an autoregressive transformer for next-token control generation, achieving 99.98% motion reproduction success with emergent ...
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