EMOSH: Expressive Motion and Shape Disentanglement for Human Animation

• URL: http://arxiv.org/abs/2606.28026v1
• Date: Fri, 26 Jun 2026 12:30:29 GMT
• Title: EMOSH: Expressive Motion and Shape Disentanglement for Human Animation
• Abstract summary: We propose EMOSH, a novel framework for high-fidelity controllable human video generation.<n>By explicitly disentangling shape and pose parameters, we resolve the body shape leakage issue.<n>We also propose a Coarse-to-Fine Hybrid Motion Injection strategy, enabling more fine-grained control over expressions and gestures.
• Score: 30.355109184821686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-fidelity and expressive controllable human animation is essential for content creation and digital avatar applications. However, existing methods face a dilemma between expressiveness and disentanglement. Mainstream 2D pose-conditioned approaches suffer from "motion-shape entanglement", leading to the leakage of the driving subject's body shape. Conversely, methods relying on 3D priors (e.g., SMPL) achieve geometric disentanglement but struggle to capture facial expressions and complex gestures, resulting in rigid animations. To this end, we propose EMOSH, a novel framework for high-fidelity controllable human video generation. First, an Expressive Human Model (EHM) is introduced as the core control representation. By explicitly disentangling shape and pose parameters, we fundamentally resolve the body shape leakage issue. Alongside this, a robust motion tracker is designed to accurately estimate EHM parameters from video. Second, we propose a Coarse-to-Fine Hybrid Motion Injection strategy, enabling more fine-grained control over expressions and gestures. Furthermore, we introduce a Spatially-Aligned Conditioning mechanism to bridge the domain gap between training and inference, improving identity consistency. Extensive experiments demonstrate that EMOSH outperforms previous methods in both self-driven and cross-driven scenarios, producing high-fidelity videos with vivid expressions while maintaining shape disentanglement.

Related papers

• 3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement [51.22216867938034]
  We present a 3D motion animation framework that controls both human motion trajectories within a reconstructed 3D environment for video generation.<n>We design a viewpoint-adaptive latent fusion mechanism to inject point-cloud geometric priors through scene-visibility masking into the generative process.<n>Experiments on two standard human image animation benchmark datasets demonstrate remarkable improvements of our method over the state of the arts in related video generation metics.
  arXiv  Detail & Related papers  (2026-06-29T16:22:54Z)
• IM-Animation: An Implicit Motion Representation for Identity-decoupled Character Animation [58.297199313494]
  Implicit methods capture motion semantics directly from driving video, but suffer from identity leakage and entanglement between motion and appearance.<n>We propose a novel implicit motion representation that compresses per-frame motion into compact 1D motion tokens.<n>Our methodology employs a three-stage training strategy to enhance the training efficiency and ensure high fidelity.
  arXiv  Detail & Related papers  (2026-02-07T11:17:20Z)
• UniMo: Unifying 2D Video and 3D Human Motion with an Autoregressive Framework [54.337290937468175]
  We propose UniMo, an autoregressive model for joint modeling of 2D human videos and 3D human motions within a unified framework.<n>We show that our method simultaneously generates corresponding videos and motions while performing accurate motion capture.
  arXiv  Detail & Related papers  (2025-12-03T16:03:18Z)
• SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation [50.792027578906804]
  We introduce SteadyDancer, an Image-to-Video (R2V) paradigm-based framework that achieves harmonized and coherent animation.<n> Experiments demonstrate that SteadyDancer achieves state-of-the-art performance in both appearance fidelity and motion control.
  arXiv  Detail & Related papers  (2025-11-24T17:15:55Z)
• DreamActor-M1: Holistic, Expressive and Robust Human Image Animation with Hybrid Guidance [9.898947423344884]
  We propose a diffusion transformer (DiT) based framework, DreamActor-M1, with hybrid guidance to overcome limitations.<n>For motion guidance, our hybrid control signals that integrate implicit facial representations, 3D head spheres, and 3D body skeletons achieve robust control of facial expressions and body movements.<n>Experiments demonstrate that our method outperforms the state-of-the-art works, delivering expressive results for portraits, upper-body, and full-body generation.
  arXiv  Detail & Related papers  (2025-04-02T13:30:32Z)
• X-Dyna: Expressive Dynamic Human Image Animation [49.896933584815926]
  X-Dyna is a zero-shot, diffusion-based pipeline for animating a single human image.<n>It generates realistic, context-aware dynamics for both the subject and the surrounding environment.
  arXiv  Detail & Related papers  (2025-01-17T08:10:53Z)
• Shape Conditioned Human Motion Generation with Diffusion Model [0.0]
  We propose a Shape-conditioned Motion Diffusion model (SMD), which enables the generation of motion sequences directly in mesh format. We also propose a Spectral-Temporal Autoencoder (STAE) to leverage cross-temporal dependencies within the spectral domain.
  arXiv  Detail & Related papers  (2024-05-10T19:06:41Z)
• FAAC: Facial Animation Generation with Anchor Frame and Conditional Control for Superior Fidelity and Editability [14.896554342627551]
  We introduce a facial animation generation method that enhances both face identity fidelity and editing capabilities. This approach incorporates the concept of an anchor frame to counteract the degradation of generative ability in original text-to-image models. Our method's efficacy has been validated on multiple representative DreamBooth and LoRA models.
  arXiv  Detail & Related papers  (2023-12-06T02:55:35Z)
• TapMo: Shape-aware Motion Generation of Skeleton-free Characters [64.83230289993145]
  We present TapMo, a Text-driven Animation Pipeline for Motion in a broad spectrum of skeleton-free 3D characters. TapMo comprises two main components - Mesh Handle Predictor and Shape-aware Diffusion Module.
  arXiv  Detail & Related papers  (2023-10-19T12:14:32Z)

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.