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Motion Flow Matching for Human Motion Synthesis and Editing
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Human motion synthesis is a fundamental task in computer animation. Recent methods based on diffusion models or GPT structure demonstrate commendable performance but exhibit drawbacks in terms of slow sampling speeds and error accumulation. In this paper, we propose \emph{Motion Flow Matching}, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in motion editing applications. Our method reduces the sampling complexity from thousand steps in previous diffusion models to just ten steps, while achieving comparable performance in text-to-motion and action-to-motion generation benchmarks. Noticeably, our approach establishes a new state-of-the-art Fr\'echet Inception Distance on the KIT-ML dataset. What is more, we tailor a straightforward motion editing paradigm named \emph{sampling trajectory rewriting} leveraging the ODE-style generative models and apply it to various editing scenarios including motion prediction, motion in-between prediction, motion interpolation, and upper-body editing. Our code will be released.
Forward citations
Cited by 8 Pith papers
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MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation
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...
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Absolute Coordinates Make Motion Generation Easy
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.
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Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression
A masked-autoregressive diffusion model trained on a compact essential-feature latent space claims state-of-the-art text-to-motion generation under a new essential-dimension evaluation protocol.
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CustomDance: Customized 3D Dance Generation with Coarse-to-Fine Human-Centered Interactive Control
CustomDance combines an MLLM-based choreographic planner, multimodal dance-phrase retrieval, and diffusion inpainting into one three-stage interactive system for user-customized 3D dance generation.
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SubtleTalk: Generating Controllable Weakly-correlated Facial Dynamics for 3D Talking Heads via Residual Flow Matching
SubtleTalk generates controllable 3D talking-head motion by adding a deterministic lip prior and residual flow matching for weakly speech-correlated eyebrow, eyelid, and head dynamics, trained on a new 73.8-hour pseud...
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PlanMoGPT: Flow-Enhanced Progressive Planning for Text to Motion Synthesis
PlanMoGPT combines progressive coarse-to-fine token planning with a flow-enhanced motion tokenizer to achieve state-of-the-art text-to-motion generation, especially on long sequences.
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Fast and Robust Visuomotor Riemannian Flow Matching Policy
A stable Riemannian flow matching policy (SRFMP) that converges to the target action distribution on manifolds, evaluated on ten robotic tasks against diffusion and consistency baselines.
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Real-Time Person Image Synthesis Using a Flow Matching Model
A flow matching transformer generates pose-guided person images about twice as fast as prior diffusion methods while keeping image quality close.
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