MorphoStyle: Motion Style Transfer with Morphology Control

• URL: http://arxiv.org/abs/2609.14189v1
• Date: Sat, 12 Sep 2026 23:40:27 GMT
• Title: MorphoStyle: Motion Style Transfer with Morphology Control
• Abstract summary: We present MorphoStyle, a framework for shape-aware motion style transfer.<n>MorphoStyle is built on a shape-conditioned Finite-Scalar-Quantization Variational Auto-Encoder.
• Score: 15.706942166849288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human motion may be viewed as a combination of action content, style, and body morphology. Existing motion style transfer methods transfer a reference style onto a content motion while assuming a canonical body, whereas shape-aware motion generators adapt motion to a target shape without explicit style control. This separation of motion style and shape (morphology) makes it difficult to generate stylized motions for non-canonical bodies; naively combining a style transfer module with a shape-aware generator often leaks action content from the style reference and disrupts shape-consistent kinematics. We present MorphoStyle, a framework for shape-aware motion style transfer that is built on a shape-conditioned Finite-Scalar-Quantization Variational Auto-Encoder (FSQ-VAE). The key contribution is to pose the desired style transfer as modular latent disentanglement comprising: (i) a contrastive style encoder that extracts content-decoupled style embeddings; (ii) a text-guided style-routing mechanism that locates style-relevant joints in a text-motion feature space; and (iii) a manifold preserving style modulator that injects discriminative style embeddings in content features as a temporally-gated low-rank offset. Extensive experiments on benchmark datasets demonstrate that MorphoStyle outperforms competing baselines in terms of both shape control and motion style transfer, while simultaneously providing quantitative shape control. For more details, please see project website: https://github.com/funkdub/MorphoStyle.

Related papers

• Flexible Motion Generation from Language and Style References [8.863552570164048]
  We introduce FlexMoGen, a framework for flexible human motion synthesis conditioned on both natural language descriptions and motion style references.<n>A style example clip supplements the text by conveying nuanced motion characteristics directly, enabling the model to preserve high-level intent.<n>Experiments show that FlexMoGen achieves the best balance between content fidelity and style reflection.
  arXiv  Detail & Related papers  (2026-09-07T22:28:44Z)
• VISTA: Triplet-Supervised Video Style Transfer with Diffusion Transformers [55.10832054417015]
  We introduce VISTA-1000, a dataset with 1,000 styles and motion-aligned triplets of style reference, clean video, and stylized video.<n>We propose a diffusion-transformer-based in-context video style transfer framework with a lightweight style adapter for robust style extraction.
  arXiv  Detail & Related papers  (2026-05-17T08:03:53Z)
• VQ-Style: Disentangling Style and Content in Motion with Residual Quantized Representations [21.963237916505175]
  We propose a novel method for effective disentanglement of the style and content in human motion data.<n>Our approach is guided by the insight that content corresponds to coarse motion attributes while style captures the finer, expressive details.<n>We harness this disentangled representation using our simple and effective inference-time technique Quantized Code Swapping.
  arXiv  Detail & Related papers  (2026-02-02T16:58:17Z)
• ClusterStyle: Modeling Intra-Style Diversity with Prototypical Clustering for Stylized Motion Generation [33.75564496181951]
  We propose a clustering-based framework, ClusterStyle, to address intra-style diversity.<n>We leverage a set of prototypes to model diverse style patterns across motions belonging to the same style category.<n>Our approach outperforms existing state-of-the-art models in stylized motion generation and motion style transfer.
  arXiv  Detail & Related papers  (2025-12-02T06:24:14Z)
• AStF: Motion Style Transfer via Adaptive Statistics Fusor [58.660938790014455]
  We propose a novel Adaptive Statistics Fusor (AStF) which consists of Style Distemporalment Module (SDM) and High-Order Multi-Statistics Attention (HO-SAttn)<n> Experimental results show that, by providing a more comprehensive model, our proposed AStF shows proficiency in motion style over state-of-the-arts techniques.
  arXiv  Detail & Related papers  (2025-11-06T08:51:24Z)
• StyleMotif: Multi-Modal Motion Stylization using Style-Content Cross Fusion [14.213279927964903]
  StyleMotif is a novel Stylized Motion Latent Diffusion model.<n>It generates motion conditioned on both content and style from multiple modalities.
  arXiv  Detail & Related papers  (2025-03-27T17:59:46Z)
• SOYO: A Tuning-Free Approach for Video Style Morphing via Style-Adaptive Interpolation in Diffusion Models [54.641809532055916]
  We introduce SOYO, a novel diffusion-based framework for video style morphing.<n>Our method employs a pre-trained text-to-image diffusion model without fine-tuning, combining attention injection and AdaIN to preserve structural consistency.<n>To harmonize across video frames, we propose a novel adaptive sampling scheduler between two style images.
  arXiv  Detail & Related papers  (2025-03-10T07:27:01Z)
• SMooDi: Stylized Motion Diffusion Model [46.293854851116215]
  We introduce a novel Stylized Motion Diffusion model, dubbed SMooDi, to generate stylized motion driven by content texts and style sequences. Our proposed framework outperforms existing methods in stylized motion generation.
  arXiv  Detail & Related papers  (2024-07-17T17:59:42Z)
• MoST: Motion Style Transformer between Diverse Action Contents [23.62426940733713]
  We propose a novel motion style transformer that effectively disentangles style from content and generates a plausible motion with transferred style from a source motion. Our method outperforms existing methods and demonstrates exceptionally high quality, particularly in motion pairs with different contents, without the need for post-processing.
  arXiv  Detail & Related papers  (2024-03-10T14:11:25Z)
• MotionCrafter: One-Shot Motion Customization of Diffusion Models [66.44642854791807]
  We introduce MotionCrafter, a one-shot instance-guided motion customization method. MotionCrafter employs a parallel spatial-temporal architecture that injects the reference motion into the temporal component of the base model. During training, a frozen base model provides appearance normalization, effectively separating appearance from motion.
  arXiv  Detail & Related papers  (2023-12-08T16:31:04Z)
• Unpaired Motion Style Transfer from Video to Animation [74.15550388701833]
  Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation. We present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels. Our framework is able to extract motion styles directly from videos, bypassing 3D reconstruction, and apply them to the 3D input motion.
  arXiv  Detail & Related papers  (2020-05-12T13:21:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.