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LaserHuman: Language-guided Scene-aware Human Motion Generation in Free Environment

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arxiv 2403.13307 v2 pith:VTRTQNPX submitted 2024-03-20 cs.CV

Peishan Cong , Ziyi Wang , Zhiyang Dou , Yiming Ren , Wei Yin , Kai Cheng , Yujing Sun , Xiaoxiao Long
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Xinge Zhu Yuexin Ma
This is my paper · ORCID
classification cs.CV
keywords humangenerationlaserhumanmotiondatasetsexistinggreatlanguage-guided
verification ladder T0 review T1 audit T2 compute T3 formal
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Language-guided scene-aware human motion generation has great significance for entertainment and robotics. In response to the limitations of existing datasets, we introduce LaserHuman, a pioneering dataset engineered to revolutionize Scene-Text-to-Motion research. LaserHuman stands out with its inclusion of genuine human motions within 3D environments, unbounded free-form natural language descriptions, a blend of indoor and outdoor scenarios, and dynamic, ever-changing scenes. Diverse modalities of capture data and rich annotations present great opportunities for the research of conditional motion generation, and can also facilitate the development of real-life applications. Moreover, to generate semantically consistent and physically plausible human motions, we propose a multi-conditional diffusion model, which is simple but effective, achieving state-of-the-art performance on existing datasets.

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Forward citations

Cited by 5 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. 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.

  3. EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal

    cs.RO 2026-07 conditional novelty 6.0 of 10

    EgoHTR is a 55-sequence, 150k-frame egocentric 4D human-terrain dataset with a reconstruction pipeline, MoCap-validated benchmark, and perceptive locomotion policies deployed on a Unitree G1.

  4. Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A 7B text-to-motion model trained on the new 2M-clip MotionMillion dataset is reported to generalize zero-shot to complex, out-of-domain prompts.

  5. CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects

    cs.GR 2025-05 conditional novelty 6.0 of 10

    CoDA generates coordinated whole-body articulated-object manipulation by optimizing the noise of three decoupled diffusion models, guided by BPS-based end-effector and object trajectories.

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