TeMuDance enables text-based semantic control over music-conditioned dance generation by using motion as a bridge to align existing unpaired datasets and training a lightweight text branch on a frozen diffusion backbone with noise-filtered supervision.
Motion anything: Any to motion generation
9 Pith papers cite this work. Polarity classification is still indexing.
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ViBES introduces a speech-language-behavior model using modality-specific transformer experts that jointly generates dialogue and 3D body actions, showing gains over separate co-speech and text-to-motion baselines on multi-turn metrics.
AnyMo is a masked-modeling framework for any-modality human motion generation trained on the new OmniHuMo dataset of 5,000+ hours of multimodal motion sequences.
MSCoT uses multi-scale hierarchical token prediction, multi-scale guidance, and a token refiner to deliver SOTA text-to-motion control with 48% FID gain, 61% lower error, and 10x faster inference on HumanML3D.
AnchorRoute couples anchor-conditioned generation via AnchorKV on a frozen text-to-motion diffusion prior with residual-routed refinement through RouteSolver on piecewise-affine interval bases.
PresentAgent-2 generates query-driven multimodal presentation videos with research grounding, supporting single-speaker, multi-speaker discussion, and interactive question-answering modes.
SkelMo introduces a category-agnostic diffusion framework for skeletal motion generation from 2D videos, trained on a new dataset of ~20,000 rigged 3D animations with a structural-semantic injection mechanism.
UniMesh unifies 3D mesh generation and understanding in one model via a Mesh Head interface, Chain of Mesh iterative editing, and an Actor-Evaluator self-reflection loop.
MOGO introduces MoSA-VQ residual quantization and RQHC-Transformer for efficient real-time text-to-3D-motion generation with competitive quality on HumanML3D, KIT-ML and CMP.
citing papers explorer
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TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation
TeMuDance enables text-based semantic control over music-conditioned dance generation by using motion as a bridge to align existing unpaired datasets and training a lightweight text branch on a frozen diffusion backbone with noise-filtered supervision.
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ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body
ViBES introduces a speech-language-behavior model using modality-specific transformer experts that jointly generates dialogue and 3D body actions, showing gains over separate co-speech and text-to-motion baselines on multi-turn metrics.
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AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling
AnyMo is a masked-modeling framework for any-modality human motion generation trained on the new OmniHuMo dataset of 5,000+ hours of multimodal motion sequences.
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Multi-scale Coarse-to-fine Modeling for Test-time Human Motion Control
MSCoT uses multi-scale hierarchical token prediction, multi-scale guidance, and a token refiner to deliver SOTA text-to-motion control with 48% FID gain, 61% lower error, and 10x faster inference on HumanML3D.
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AnchorRoute: Human Motion Synthesis with Interval-Routed Sparse Contro
AnchorRoute couples anchor-conditioned generation via AnchorKV on a frozen text-to-motion diffusion prior with residual-routed refinement through RouteSolver on piecewise-affine interval bases.
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PresentAgent-2: Towards Generalist Multimodal Presentation Agents
PresentAgent-2 generates query-driven multimodal presentation videos with research grounding, supporting single-speaker, multi-speaker discussion, and interactive question-answering modes.
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SkelMo: Universal Skeletal Motion Generation for 3D Rigged Shapes
SkelMo introduces a category-agnostic diffusion framework for skeletal motion generation from 2D videos, trained on a new dataset of ~20,000 rigged 3D animations with a structural-semantic injection mechanism.
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UniMesh: Unifying 3D Mesh Understanding and Generation
UniMesh unifies 3D mesh generation and understanding in one model via a Mesh Head interface, Chain of Mesh iterative editing, and an Actor-Evaluator self-reflection loop.
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MOGO: Residual Quantized Hierarchical Causal Transformer for High-Quality and Real-Time 3D Human Motion Generation
MOGO introduces MoSA-VQ residual quantization and RQHC-Transformer for efficient real-time text-to-3D-motion generation with competitive quality on HumanML3D, KIT-ML and CMP.