Latent Dynamics for Full Body Avatar Animation

• URL: http://arxiv.org/abs/2605.21478v1
• Date: Wed, 20 May 2026 17:58:03 GMT
• Title: Latent Dynamics for Full Body Avatar Animation
• Abstract summary: We augment a pose-conditioned 3D Gaussian avatar with a transformer-based decoder and a dynamics residual latent.<n>The model decomposes each update into driving, restoring, and dissipative forces, producing temporally coherent, history-dependent rollouts.<n>Across nine captured sequences of everyday motion with diverse loose garments, quantitative metrics and a perceptual user study show improved animation quality over recent data-driven baselines.
• Score: 18.84485573885924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pose-driven full-body avatars built on neural rendering produce high-quality novel views of a captured subject. Yet loose clothing and other dynamic elements deform in ways pose alone cannot explain: the same pose can correspond to many different states, because their motion depends on history, inertia, and contact. Explicit simulation and layered-garment methods can model such dynamics, but they require either a dedicated garment template, which raw multi-view capture does not naturally provide, or a test-time physics simulator with non-trivial runtime cost. A parallel line of work learns data-driven clothing avatars that avoid explicit garment layers. These methods add an auxiliary latent for variation beyond pose; at inference, they fix it, regress it from pose, or retrieve it from training data, without explicitly modeling how the latent evolves with its own dynamics. Additionally, even in everyday motion with loose clothing, existing architectures often struggle to capture fine-grained detail, producing blurry renderings and temporal artifacts. We augment a pose-conditioned 3D Gaussian avatar with a transformer-based decoder and a dynamics residual latent that captures temporal appearance and geometry variation beyond the driving signals. At inference, a learned latent dynamics model evolves the residual latent from a short pose history and the previous latent state. The model decomposes each update into driving, restoring, and dissipative forces, producing temporally coherent, history-dependent rollouts with negligible added cost. Different initial conditions yield diverse yet plausible motion trajectories, and the force decomposition exposes controls such as stiffness. Across nine captured sequences of everyday motion with diverse loose garments, quantitative metrics and a perceptual user study show improved animation quality over recent data-driven baselines.

Related papers

• AvatarDynamizer: From Static to Dynamic Human Avatars via Generative Dynamic Textures [81.30642929096783]
  AvatarDynamizer is a generative method that transforms an off-the-shelf static 3D avatar into a controllable, realistic, and multi-view-consistent 4D avatar.<n>Our method effectively animates static avatars with faithful surface dynamics and outperforms competing generalizable methods in visual fidelity.
  arXiv  Detail & Related papers  (2026-08-20T11:10:53Z)
• DSAR: Dual-Stream Autoregressive Modeling of Temporal Cloth Dynamics for Photorealistic Animatable Avatars [7.198815179389743]
  We introduce a dual-stream autoregressive framework that explicitly models both observable geometric information and implicit internal state.<n> Experiments on challenging datasets demonstrate significant improvements in rendering quality, temporal consistency, and generalization to motion patterns beyond training distributions.
  arXiv  Detail & Related papers  (2026-08-11T05:19:21Z)
• Autoregressive Appearance Prediction for 3D Gaussian Avatars [67.37159422872219]
  We propose a 3D Splatting avatar model with a spatial backbone conditioned on both pose and an appearance latent.<n>The latent is learned during training by an encoder, yielding a compact representation that improves reconstruction quality.<n>Our method delivers a robust and practical path to high-fidelity, stable avatar driving.
  arXiv  Detail & Related papers  (2026-04-01T14:07:14Z)
• RealityAvatar: Towards Realistic Loose Clothing Modeling in Animatable 3D Gaussian Avatars [4.332718737928592]
  We propose RealityAvatar, an efficient framework for high-fidelity digital human modeling, specifically targeting loosely dressed avatars.<n>By incorporating a motion trend module and a latentbone encoder, we explicitly model pose-dependent deformations and temporal variations in clothing behavior.<n>Our method significantly enhances structural fidelity and perceptual quality in dynamic human reconstruction, particularly in non-rigid regions.
  arXiv  Detail & Related papers  (2025-04-02T09:59:12Z)
• Within the Dynamic Context: Inertia-aware 3D Human Modeling with Pose Sequence [47.16903508897047]
  In this study, we elucidate that variations in human appearance depend not only on the current frame's pose condition but also on past pose states. We introduce Dyco, a novel method utilizing the delta pose sequence representation for non-rigid deformations. In addition, our inertia-aware 3D human method can unprecedentedly simulate appearance changes caused by inertia at different velocities.
  arXiv  Detail & Related papers  (2024-03-28T06:05:14Z)
• AniDress: Animatable Loose-Dressed Avatar from Sparse Views Using Garment Rigging Model [58.035758145894846]
  We introduce AniDress, a novel method for generating animatable human avatars in loose clothes using very sparse multi-view videos. A pose-driven deformable neural radiance field conditioned on both body and garment motions is introduced, providing explicit control of both parts. Our method is able to render natural garment dynamics that deviate highly from the body and well to generalize to both unseen views and poses.
  arXiv  Detail & Related papers  (2024-01-27T08:48:18Z)
• Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single Camera [49.357174195542854]
  A key challenge of learning the dynamics of the appearance lies in the requirement of a prohibitively large amount of observations. We show that our method can generate a temporally coherent video of dynamic humans for unseen body poses and novel views given a single view video.
  arXiv  Detail & Related papers  (2022-03-24T00:22:03Z)
• Dynamic Neural Garments [45.833166320896716]
  We present a solution that takes in body joint motion to directly produce realistic dynamic garment image sequences. Specifically, given the target joint motion sequence of an avatar, we propose dynamic neural garments to jointly simulate and render plausible dynamic garment appearance.
  arXiv  Detail & Related papers  (2021-02-23T17:21:21Z)
• High-Fidelity Neural Human Motion Transfer from Monocular Video [71.75576402562247]
  Video-based human motion transfer creates video animations of humans following a source motion. We present a new framework which performs high-fidelity and temporally-consistent human motion transfer with natural pose-dependent non-rigid deformations. In the experimental results, we significantly outperform the state-of-the-art in terms of video realism.
  arXiv  Detail & Related papers  (2020-12-20T16:54:38Z)

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.