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Motion Generation

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Showing 126–150 of 446 papers

TitleStatusHype
Multi-Person Interaction Generation from Two-Person Motion Priors—0
MEgoHand: Multimodal Egocentric Hand-Object Interaction Motion Generation—0
UniHM: Universal Human Motion Generation with Object Interactions in Indoor Scenes—0
Towards Robust and Controllable Text-to-Motion via Masked Autoregressive Diffusion—0
MARRS: Masked Autoregressive Unit-based Reaction Synthesis—0
MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generation—0
Text-driven Motion Generation: Overview, Challenges and Directions—0
ReAlign: Bilingual Text-to-Motion Generation via Step-Aware Reward-Guided Alignment—0
StableMotion: Training Motion Cleanup Models with Unpaired Corrupted Data—0
Efficient 3D Full-Body Motion Generation from Sparse Tracking Inputs with Temporal Windows—0
TSTMotion: Training-free Scene-aware Text-to-motion Generation—0
Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis—0
GENMO: A GENeralist Model for Human MOtion—0
Sketch2Anim: Towards Transferring Sketch Storyboards into 3D Animation—0
HUMOTO: A 4D Dataset of Mocap Human Object Interactions—0
EchoMask: Speech-Queried Attention-based Mask Modeling for Holistic Co-Speech Motion Generation—0
DanceMosaic: High-Fidelity Dance Generation with Multimodal Editability—0
Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions—0
FlowMotion: Target-Predictive Conditional Flow Matching for Jitter-Reduced Text-Driven Human Motion Generation—0
SIGHT: Single-Image Conditioned Generation of Hand Trajectories for Hand-Object Interaction—0
GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain—0
StyleMotif: Multi-Modal Motion Stylization using Style-Content Cross Fusion—0
ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer—0
Guiding Human-Object Interactions with Rich Geometry and Relations—0
Direct Post-Training Preference Alignment for Multi-Agent Motion Generation Models Using Implicit Feedback from Pre-training Demonstrations—0
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