The paper introduces a two-stage framework to incorporate the animation principle of exaggeration into motion generative models. During training, it fine‑tunes pre‑trained text‑to‑motion models on a curated exaggeration dataset. At inference, it uses a mathematical formulation based on dynamic movement primitives to guide diffusion and flow‑matching models toward more expressive, exaggerated motions while maintaining physical plausibility and the original motion intent.
By Amirhossein Zamani, Arianna Rampini, Bruno Roy
arXiv:2609.23817v1 Announce Type: new
Abstract: We present VISTA, a two-stage framework for generating stylized 3D human motion by fusing structural content from text prompts with expressive style fr...
By Monseej Purkayastha, Anindita Ghosh, Philipp Slusallek
MorphoStyle is a new framework for shape‑aware motion style transfer that uses a shape‑conditioned FSQ‑VAE. It disentangles style from content through a contrastive style encoder, a text‑guided style‑routing mechanism, and a manifold‑preserving style modulator. Experiments on benchmark datasets show that MorphoStyle outperforms existing baselines in both shape control and motion style transfer.
By Xin Feng, Eleonora D'Arnese, Mohan Sridharan
arXiv:2609.08032v1 Announce Type: cross
Abstract: We introduce FlexMoGen, a novel framework for flexible human motion synthesis conditioned on both natural language descriptions and motion style refe...
By Kai Weixian Lan, Bodie Criswell, Briana Fedkiw, Zhan Zhang, Joseph Teran, Daniel Holden
arXiv:2608.23279v1 Announce Type: new
Abstract: Text-driven human motion synthesis has made substantial development with two core modules of motion representation and generative architecture. For rep...
By Chengqun Yang, Liang Xu, Yanping Li, Fulong Liu, Jingnan Gao, Weili Zeng, Yichao Yan
arXiv:2609.36937v1 Announce Type: cross
Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,...
By Sangeyl Lee, Seunghyun Shin, Seungho Park, Wooseok Jeon, Hae-Gon Jeon
arXiv:2412.00112v5 Announce Type: replace
Abstract: Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics a...
By Seong-Eun Hong, Soobin Lim, Juyeong Hwang, Minwook Chang, Hyeongyeop Kang
Fine-grained Human Motion Understanding with Language Models introduces FiGMo, an LLM-based model that represents motion as timestamp‑grounded skeletal poses, enabling reasoning over action order and duration. The model uses a unified pose encoder for both 2D and 3D skeletons, optionally incorporating RGB context, and relies on newly constructed pose‑to‑motion supervision to address limited pose‑level grounding. Experiments demonstrate that timestamps improve temporal localization and preserve physical timing under subsampling, leading FiGMo to achieve state‑of‑the‑art results on motion question answering, dense motion captioning, and action recognition with detected poses.
By Thomas Markhorst, Zhi-Yi Lin, Jouh Yeong Chew, Jan van Gemert, Xucong Zhang
arXiv:2609.37495v1 Announce Type: new
Abstract: Human motion generation plays an important role in applications such as character animation, virtual environments, and embodied interaction. While exis...
By Yun Chen, Munchurl Kim, Jeonghyeok Do
GHARP is a two‑stage method that animates 3D human heads in real time using a large‑scale reconstruction prior. The first offline identity stage builds a compact Gaussian representation of a subject’s geometry and appearance, while the lightweight runtime animation stage predicts expression‑dependent residuals in a semantically structured latent space. A body alignment network further refines the animation by aligning body pose and clothing, enabling state‑of‑the‑art quality on the Ava‑256 benchmark with significantly faster performance and fewer Gaussians.
By Ali Benlalah, Sepehr Johari, Patricia Vitoria, Armin Kappeler, Artem Sevastopolsky, Alexander Jung, Gabriele Fanelli, Kevin Mader, Manuel Breitenstein, Claudia Pl\"uss, Jan R\"uegg, Simon Biland, Thomas Etterlin, Dmitry Kostiaev, Mathias Deschler, Brian Amberg, Sebastian Martin
arXiv:2606. 31981v1 Announce Type: cross Abstract: Creating photorealistic, animatable 3D human avatars from monocular images still largely depends on Linear Blend Skinning (LBS) and parametric body models, which constrain expressivity and often introduce artifacts due to imperfect fitting.
By Peng Li, Rawal Khirodkar, Junxuan Li, Yuan Dong, Chen Cao, Yuan Liu, Wenhan Luo, Yike Guo, Shunsuke Saito
The paper introduces Motion Style Slider, a framework that enables continuous, endpoint‑supervised control of style intensity in human motion diffusion. By constructing a style direction in a learned motion‑style embedding space and conditioning diffusion generation with a scalar intensity, the method achieves smooth, monotonic style scaling without needing intermediate‑intensity ground truth. The approach is compatible with pretrained diffusion backbones, supports heterogeneous style datasets, and is evaluated on controllability, interpolation/extrapolation, content preservation, and motion realism.
By Chen-Chieh Liao, Yichen Peng, Yiyi Cai, Y\^ui Ono, Hiroki Hanaoka, Erwin Wu, Hideki Koike, Shuichi Kurabayashi