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Generative Human Motion Stylization in Latent Space
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Human motion stylization aims to revise the style of an input motion while keeping its content unaltered. Unlike existing works that operate directly in pose space, we leverage the latent space of pretrained autoencoders as a more expressive and robust representation for motion extraction and infusion. Building upon this, we present a novel generative model that produces diverse stylization results of a single motion (latent) code. During training, a motion code is decomposed into two coding components: a deterministic content code, and a probabilistic style code adhering to a prior distribution; then a generator massages the random combination of content and style codes to reconstruct the corresponding motion codes. Our approach is versatile, allowing the learning of probabilistic style space from either style labeled or unlabeled motions, providing notable flexibility in stylization as well. In inference, users can opt to stylize a motion using style cues from a reference motion or a label. Even in the absence of explicit style input, our model facilitates novel re-stylization by sampling from the unconditional style prior distribution. Experimental results show that our proposed stylization models, despite their lightweight design, outperform the state-of-the-art in style reenactment, content preservation, and generalization across various applications and settings. Project Page: https://murrol.github.io/GenMoStyle
Forward citations
Cited by 5 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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ClusterStyle: Modeling Intra-Style Diversity with Prototypical Clustering for Stylized Motion Generation
ClusterStyle clusters each motion style into global and local prototypes and conditions a latent diffusion model on them, improving stylized motion generation fidelity and enabling controllable within-style diversity ...
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MotionPersona: Characteristics-aware Locomotion Control
A single diffusion-based controller generates real-time character locomotion conditioned on body shape, text-described traits, and user control, plus a few-shot personalization mode.
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Motion Generation: A Survey of Generative Approaches and Benchmarks
A structured survey that categorizes recent motion generation methods by underlying generative approach and compiles datasets, metrics, and statistical trends.
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