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InterMask: 3D Human Interaction Generation via Collaborative Masked Modeling

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arxiv 2410.10010 v3 pith:7IGMSFRM submitted 2024-10-13 cs.CV

Muhammad Gohar Javed , Chuan Guo , Li Cheng , Xingyu Li This is my paper
classification cs.CV
keywords intermaskmaskedmotionhumanindividualsinteractionsmodelingtoken
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Generating realistic 3D human-human interactions from textual descriptions remains a challenging task. Existing approaches, typically based on diffusion models, often produce results lacking realism and fidelity. In this work, we introduce InterMask, a novel framework for generating human interactions using collaborative masked modeling in discrete space. InterMask first employs a VQ-VAE to transform each motion sequence into a 2D discrete motion token map. Unlike traditional 1D VQ token maps, it better preserves fine-grained spatio-temporal details and promotes spatial awareness within each token. Building on this representation, InterMask utilizes a generative masked modeling framework to collaboratively model the tokens of two interacting individuals. This is achieved by employing a transformer architecture specifically designed to capture complex spatio-temporal inter-dependencies. During training, it randomly masks the motion tokens of both individuals and learns to predict them. For inference, starting from fully masked sequences, it progressively fills in the tokens for both individuals. With its enhanced motion representation, dedicated architecture, and effective learning strategy, InterMask achieves state-of-the-art results, producing high-fidelity and diverse human interactions. It outperforms previous methods, achieving an FID of $5.154$ (vs $5.535$ of in2IN) on the InterHuman dataset and $0.399$ (vs $5.207$ of InterGen) on the InterX dataset. Additionally, InterMask seamlessly supports reaction generation without the need for model redesign or fine-tuning.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation

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    Distilling frozen Motion-JEPA features into a compact 32-D latent whose geometry is coupled to the decoder lets a standard non-autoregressive flow-matching DiT reach state-of-the-art text-to-motion quality on HumanML3...

  2. MIME: Multimodal Interactive Motion Encoder

    cs.CV 2026-07 conditional novelty 6.0 of 10

    MIME, a co-attention encoder with explicit relational features and curriculum contrastive training, improves two-person text-motion retrieval and transfers to downstream generation.

  3. PhysiGen: Integrating Collision-Aware Physical Constraints for High-Fidelity Human-Human Interaction Generation

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    PhysiGen reduces interpenetration in text-driven 3D human interaction generation by simplifying meshes to geometric primitives for fast collision detection and guiding optimization with collision regions.

  4. Stability-Driven Motion Generation for Object-Guided Human-Human Co-Manipulation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    A flow-matching model derives manipulation strategies from object affordance, adds an adversarial interaction prior, and uses stability simulation to generate natural, effective human-human co-manipulation motions.

  5. MoLingo: Motion-Language Alignment for Text-to-Human Motion Generation

    cs.CV 2025-12 conditional novelty 6.0 of 10

    A semantically aligned latent space plus multi-token cross-attention conditioning sets a new state of the art in text-to-human-motion generation on HumanML3D.

  6. TS-Mask VLA: 2D Temporal-Spatial Masking for Vision-Language-Action Model with Effective Bridging

    cs.RO 2026-07 conditional novelty 5.0 of 10

    A 0.5B VLA with bridge-conditioned discrete diffusion and 2D temporal–spatial action masking reaches 95.7% LIBERO success and 4.19 CALVIN average length.

  7. Exploring Motion-Language Alignment for Text-driven Motion Generation

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    MLA-Gen advances text-driven motion synthesis by aligning global motion patterns with fine-grained text semantics and mitigating attention sink effects via new masking techniques.

  8. Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    Human-X jointly predicts actions and reactions in real time to produce physically plausible human-machine interaction motion.

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