DrawMotion is a diffusion-based framework that fuses text and hand-drawn stickman conditions via a Multi-Condition Module and training-free guidance to generate 3D human motions.
Unified human-scene interaction via prompted chain-of- contacts
5 Pith papers cite this work. Polarity classification is still indexing.
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The Weightlessness Mechanism lets humanoid robots imitate non-self-stabilizing motions by dynamically relaxing specific joints to exploit passive environmental contacts, generalizing from single demonstrations to varied setups.
NaP-Control uses RL to directly predict optimized diffusion noise from a task-agnostic prior, enabling fast inference and higher success rates for versatile whole-body character control while preserving motion quality.
ALAS disentangles environment and self-state streams via bio-inspired modules to deliver 23% higher subtask success and 29% better execution efficiency on long-horizon HSI tasks.
A literature review of pHHI that proposes a taxonomy of interaction types by modality and engagement level while outlining pathways to integrate control, intent, and modeling for more seamless humanoid-human collaboration.
citing papers explorer
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DrawMotion: Generating 3D Human Motions by Freehand Drawing
DrawMotion is a diffusion-based framework that fuses text and hand-drawn stickman conditions via a Multi-Condition Module and training-free guidance to generate 3D human motions.
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Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot
The Weightlessness Mechanism lets humanoid robots imitate non-self-stabilizing motions by dynamically relaxing specific joints to exploit passive environmental contacts, generalizing from single demonstrations to varied setups.
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NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control
NaP-Control uses RL to directly predict optimized diffusion noise from a task-agnostic prior, enabling fast inference and higher success rates for versatile whole-body character control while preserving motion quality.
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ALAS: Adaptive Long-Horizon Action Synthesis via Async-pathway Stream Disentanglement
ALAS disentangles environment and self-state streams via bio-inspired modules to deliver 23% higher subtask success and 29% better execution efficiency on long-horizon HSI tasks.
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Toward Seamless Physical Human-Humanoid Interaction: Insights from Control, Intent, and Modeling with a Vision for What Comes Next
A literature review of pHHI that proposes a taxonomy of interaction types by modality and engagement level while outlining pathways to integrate control, intent, and modeling for more seamless humanoid-human collaboration.