Flexible Motion Generation from Language and Style References

• URL: http://arxiv.org/abs/2609.08032v1
• Date: Mon, 07 Sep 2026 22:28:44 GMT
• Title: Flexible Motion Generation from Language and Style References
• Abstract summary: 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.
• Score: 8.863552570164048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce FlexMoGen, a novel framework for flexible human motion synthesis conditioned on both natural language descriptions and motion style references. Text prompts are effective at defining semantic content, but they are often limited in capturing fine-grained style details such as timing, limb articulation, and expressive dynamics. A style example clip supplements the text by conveying these nuanced motion characteristics directly, enabling the model to preserve high-level intent while reproducing the desired stylistic traits. Given a text prompt and a style example clip, FlexMoGen generates high-quality motions that preserve semantic content while faithfully reflecting the target style, offering users greater control over the animation generation process. Unlike prior methods that rely on discrete style labels and do not generalize to long or multi-style generation, FlexMoGen learns a variational style encoder without style supervision and supports long, time-varying, multi-style synthesis. Our framework jointly pre-trains the style encoder and a text-to-motion latent diffusion model within a unified architecture, modulating motion style through a lightweight adaptation module. It integrates an efficient relative positional encoding scheme and is trained on both stylized and non-stylized datasets, enabling strong generalization to unseen text-style combinations. Experiments show that FlexMoGen achieves the best balance between content fidelity and style reflection.

Related papers

• MorphoStyle: Motion Style Transfer with Morphology Control [15.706942166849288]
  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.
  arXiv  Detail & Related papers  (2026-09-12T23:40:27Z)
• Stylized Text-to-Motion Generation via Hypernetwork-Driven Low-Rank Adaptation [15.45127792716575]
  Text-driven motion diffusion models are capable of generating realistic human motions, but text alone often struggles to express fine-level nuances of motion known as style.<n>Recent approaches have tackled this challenge by attaching a style injection mechanism to a pretrained text-driven diffusion model.<n>We propose a lightweight style conditioning framework that dynamically modulates a pretrained diffusion model through hypernetwork-generated LoRA parameters.<n> Experiments on the HumanML3D and 100STYLE datasets show state-of-the-art stylization results, while achieving improved stylization for unseen styles.
  arXiv  Detail & Related papers  (2026-05-13T10:51:54Z)
• Mixture of Style Experts for Diverse Image Stylization [73.7600741657193]
  StyleExpert is a semantic-aware framework based on the Mixture of Experts (MoE)<n>Our framework employs a unified style encoder, trained on our large-scale dataset of content-style-stylized triplets.<n>Our method adeptly handles diverse styles spanning multiple semantic levels, from shallow textures to deep semantics.
  arXiv  Detail & Related papers  (2026-03-17T15:20:19Z)
• Sissi: Zero-shot Style-guided Image Synthesis via Semantic-style Integration [57.02757226679549]
  We introduce a training-free framework that reformulates style-guided synthesis as an in-context learning task.<n>We propose a Dynamic Semantic-Style Integration (DSSI) mechanism that reweights attention between semantic and style visual tokens.<n>Experiments show that our approach achieves high-fidelity stylization with superior semantic-style balance and visual quality.
  arXiv  Detail & Related papers  (2026-01-10T16:01:14Z)
• 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)
• StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples [48.44036251656947]
  Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content.<n>We introduce StyleDistance, a novel approach to training stronger content-independent style embeddings.
  arXiv  Detail & Related papers  (2024-10-16T17:25:25Z)
• ArtWeaver: Advanced Dynamic Style Integration via Diffusion Model [73.95608242322949]
  Stylized Text-to-Image Generation (STIG) aims to generate images from text prompts and style reference images. We present ArtWeaver, a novel framework that leverages pretrained Stable Diffusion to address challenges such as misinterpreted styles and inconsistent semantics.
  arXiv  Detail & Related papers  (2024-05-24T07:19:40Z)
• Generative Human Motion Stylization in Latent Space [42.831468727082694]
  We present a novel generative model that produces diverse stylization results of a single motion (latent) code. In inference, users can opt to stylize a motion using style cues from a reference motion or a label. 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.
  arXiv  Detail & Related papers  (2024-01-24T14:53:13Z)
• StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter [78.75422651890776]
  StyleCrafter is a generic method that enhances pre-trained T2V models with a style control adapter. To promote content-style disentanglement, we remove style descriptions from the text prompt and extract style information solely from the reference image. StyleCrafter efficiently generates high-quality stylized videos that align with the content of the texts and resemble the style of the reference images.
  arXiv  Detail & Related papers  (2023-12-01T03:53:21Z)
• ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfer [57.6482608202409]
  Textual style transfer is the task of transforming stylistic properties of text while preserving meaning. We introduce a novel diffusion-based framework for general-purpose style transfer that can be flexibly adapted to arbitrary target styles. We validate the method on the Enron Email Corpus, with both human and automatic evaluations, and find that it outperforms strong baselines on formality, sentiment, and even authorship style transfer.
  arXiv  Detail & Related papers  (2023-08-29T17:36:02Z)
• GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents [3.229105662984031]
  GestureDiffuCLIP is a neural network framework for synthesizing realistic, stylized co-speech gestures with flexible style control. Our system learns a latent diffusion model to generate high-quality gestures and infuses the CLIP representations of style into the generator. Our system can be extended to allow fine-grained style control of individual body parts.
  arXiv  Detail & Related papers  (2023-03-26T03:35:46Z)

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.