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DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion

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arxiv 2309.01372 v1 pith:SULVYJAC submitted 2023-09-04 cs.CV

Yunhong Lou , Linchao Zhu , Yaxiong Wang , Xiaohan Wang , Yi Yang This is my paper
classification cs.CV
keywords motiondiversitymotionsdatasetdiversediversemotionexistinghuman
verification ladder T0 review T1 audit T2 compute T3 formal
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We present DiverseMotion, a new approach for synthesizing high-quality human motions conditioned on textual descriptions while preserving motion diversity.Despite the recent significant process in text-based human motion generation,existing methods often prioritize fitting training motions at the expense of action diversity. Consequently, striking a balance between motion quality and diversity remains an unresolved challenge. This problem is compounded by two key factors: 1) the lack of diversity in motion-caption pairs in existing benchmarks and 2) the unilateral and biased semantic understanding of the text prompt, focusing primarily on the verb component while neglecting the nuanced distinctions indicated by other words.In response to the first issue, we construct a large-scale Wild Motion-Caption dataset (WMC) to extend the restricted action boundary of existing well-annotated datasets, enabling the learning of diverse motions through a more extensive range of actions. To this end, a motion BLIP is trained upon a pretrained vision-language model, then we automatically generate diverse motion captions for the collected motion sequences. As a result, we finally build a dataset comprising 8,888 motions coupled with 141k text.To comprehensively understand the text command, we propose a Hierarchical Semantic Aggregation (HSA) module to capture the fine-grained semantics.Finally,we involve the above two designs into an effective Motion Discrete Diffusion (MDD) framework to strike a balance between motion quality and diversity. Extensive experiments on HumanML3D and KIT-ML show that our DiverseMotion achieves the state-of-the-art motion quality and competitive motion diversity. Dataset, code, and pretrained models will be released to reproduce all of our results.

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Cited by 7 Pith papers

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

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

    cs.CV 2026-07 conditional novelty 7.0 of 10

    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. UniMotion: A Unified Framework for Motion-Text-Vision Understanding and Generation

    cs.CV 2026-03 conditional novelty 7.0 of 10

    UniMotion unifies continuous human-motion, text, and RGB understanding/generation/editing in one LLM backbone via CMA-VAE, Dual-Posterior KL Alignment, and Latent Reconstruction Alignment, reporting SOTA on seven tri-...

  3. Absolute Coordinates Make Motion Generation Easy

    cs.CV 2025-05 conditional novelty 7.0 of 10

    Using absolute 3D joint coordinates with a plain Transformer and velocity-prediction diffusion outperforms the standard local-relative motion representation, improving fidelity and enabling direct control.

  4. Next-Scale Autoregressive Models for Text-to-Motion Generation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    Next-scale autoregressive modeling with cross-scale and in-scale refinements produces SOTA text-to-motion generation by enforcing coarse-to-fine causal hierarchy.

  5. Language-Guided Transformer Tokenizer for Human Motion Generation

    cs.CV 2026-02 conditional novelty 6.0 of 10

    Injecting language into the motion tokenizer yields more compact semantic tokens and state-of-the-art generation scores on HumanML3D and Motion-X.

  6. Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A dexterous VLA pretrained on a 2.5M-instance human hand motion dataset transfers skills to a real robot hand, outperforming baselines in manipulation tasks.

  7. Discrete Diffusion Models: A Unified Framework from Tokenization to Generation

    cs.LG 2026-07 unverdicted novelty 4.0 of 10

    Discrete diffusion models are re-framed as instances of a tokenization-centric, four-component design space (corruption, denoiser, objective, sampler) in a broad survey with no new experimental or theoretical results.

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