Causal Motion Diffusion Models for Autoregressive Motion Generation
Recent advances in motion diffusion models have substantially improved the realism of human motion synthesis. However, existing approaches either rely on full-sequence diffusion models with bidirectional generation, which limits temporal causality and real-time applicability, or autoregressive models that suffer from instability and cumulative errors. In this work, we present Causal Motion Diffusion Models (CMDM), a unified framework for autoregressive motion generation based on a causal diffusion transformer that operates in a semantically aligned latent space. CMDM builds upon a Motion-Language-Aligned Causal VAE (MAC-VAE), which encodes motion sequences into temporally causal latent representations. On top of this latent representation, an autoregressive diffusion transformer is trained using causal diffusion forcing to perform temporally ordered denoising across motion frames. To achieve fast inference, we introduce a frame-wise sampling schedule with causal uncertainty, where each subsequent frame is predicted from partially denoised previous frames. The resulting framework supports high-quality text-to-motion generation, streaming synthesis, and long-horizon motion generation at interactive rates. Experiments on HumanML3D and SnapMoGen demonstrate that CMDM outperforms existing diffusion and autoregressive models in both semantic fidelity and temporal smoothness, while substantially reducing inference latency.
69a10abaa13deaa44944895d
Qing Yu
69a10abaa13deaa44944895e
Akihisa Watanabe
claimed_verified
2026-03-03T10:42:57.282Z
630cab9615433862cfc53566
/avatars/ab1698b84fcbf99eb3b77003deceba03.svg
Akihisa Watanabe
Akihisa-Watanabe
user
Akihisa-Watanabe
69a10abaa13deaa44944895f
Kent Fujiwara
Causal Motion Diffusion Models for Autoregressive Motion Generation
Recent advances in motion diffusion models have substantially improved the realism of human motion synthesis. However, existing approaches either rely on full-sequence diffusion models with bidirectional generation, which limits temporal causality and real-time applicability, or autoregressive models that suffer from instability and cumulative errors. In this work, we present Causal Motion Diffusion Models (CMDM), a unified framework for autoregressive motion generation based on a causal diffusion transformer that operates in a semantically aligned latent space. CMDM builds upon a Motion-Language-Aligned Causal VAE (MAC-VAE), which encodes motion sequences into temporally causal latent representations. On top of this latent representation, an autoregressive diffusion transformer is trained using causal diffusion forcing to perform temporally ordered denoising across motion frames. To achieve fast inference, we introduce a frame-wise sampling schedule with causal uncertainty, where each subsequent frame is predicted from partially denoised previous frames. The resulting framework supports high-quality text-to-motion generation, streaming synthesis, and long-horizon motion generation at interactive rates. Experiments on HumanML3D and SnapMoGen demonstrate that CMDM outperforms existing diffusion and autoregressive models in both semantic fidelity and temporal smoothness, while substantially reducing inference latency.
Causal Motion Diffusion Models introduce a unified framework for autoregressive motion generation using a causal diffusion transformer in a semantically aligned latent space, enabling fast, high-quality text-to-motion synthesis with improved temporal smoothness.
motion diffusion models causal diffusion transformer latent space Motion-Language-Aligned Causal VAE autoregressive motion generation causal diffusion forcing temporally ordered denoising frame-wise sampling schedule causal uncertainty text-to-motion generation streaming synthesis long-horizon motion generation