MAD-HOI: Masked Autoregressive Diffusion for Generating Articulated Hand Object Interactions from Text

• URL: http://arxiv.org/abs/2608.10162v2
• Date: Sat, 15 Aug 2026 05:45:33 GMT
• Title: MAD-HOI: Masked Autoregressive Diffusion for Generating Articulated Hand Object Interactions from Text
• Abstract summary: We present a model performing Masked Autoregression with Diffusion for HOI generation (MAD-HOI)<n>Our method starts by encoding hand and object motions in a continuous latent space while keeping them disentangled to maintain stream-wise control.<n> MAD-HOI is capable of motion generation for atomic and composite articulated sequences, conditioned motion completion and infilling, as well as EOM (End of Motion) prediction from a single training objective.
• Score: 4.764824772285089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Methods for text-based generation of hand-object interaction (HOI) sequences primarily focus on producing smooth, physically plausible trajectories. A truly utilitarian method should additionally support variable-length generation, composite motion sequences, motion completion and infilling, and reliable termination without compromising physical plausibility. Standard diffusion models for HOI generation are typically trained only for text-to-motion generation on atomic motions and require the motion length to be specified a-priori. Autoregressive (AR) methods provide greater sequence-level flexibility, but commonly depend on discrete motion codes, which can lose contact-sensitive motion detail. To address these key limitations, we present a model performing Masked Autoregression with Diffusion for HOI generation (MAD-HOI). Our method starts by encoding hand and object motions in a continuous latent space while keeping them disentangled to maintain stream-wise control. This is followed by a masked autoregressive transformer to predict context features that condition a flow-matching head. MAD-HOI is capable of motion generation for atomic and composite articulated sequences, conditioned motion completion and infilling, as well as EOM (End of Motion) prediction from a single training objective. We provide comprehensive evaluations for these capabilities and benchmark our method on the ARCTIC and GRAB datasets. Our experiments demonstrate that our method generates more diverse and physically plausible interactions compared to other open-sourced baseline methods.

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