MotionDreamer: Universal Skeletal Motion Generation for 3D Rigged Shapes

• URL: http://arxiv.org/abs/2606.01518v1
• Date: Mon, 01 Jun 2026 00:42:31 GMT
• Title: MotionDreamer: Universal Skeletal Motion Generation for 3D Rigged Shapes
• Abstract summary: MotionDreamer is a diffusion-based framework for category-agnostic skeletal animation generation from 2D video guidance.<n>We propose a structural-semantic injection mechanism to bridge the gap between 2D visual motion cues and heterogeneous 3D skeletal structures.<n>Our approach significantly outperforms existing methods, setting a new state-of-the-art benchmark for robust and efficient 4D asset generation.
• Score: 52.45449797329548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motion generation for rigged shapes is vital for scalable 4D asset production. However, template-based methods are limited by specific topologies and fail to generalize across diverse morphologies. Conversely, per-case optimization is computationally expensive, susceptible to local optima, and highly sensitive to viewpoint-induced ambiguities. In this paper, we present MotionDreamer, a diffusion-based framework designed for category-agnostic skeletal animation generation from 2D video guidance. To overcome the scarcity of high-quality training data, we have curated a large-scale dynamic dataset comprising approximately 20,000 diverse 3D models, each featuring complete textures, skeletal rigging, and a wide array of comprehensive animation sequences. To bridge the kinematic gap between 2D visual motion cues and heterogeneous 3D skeletal structures, we propose a structural-semantic injection mechanism. Our model integrates texture and semantic attributes directly into skeletal joint representations. This allows it to map perceived visual dynamics to specific joint hierarchies and their functional roles. This enables MotionDreamer to synthesize high-fidelity animations that maintain anatomical consistency across a vast range of unseen categories, from existing biological species to fantastical beings. Extensive experiments demonstrate that our approach significantly outperforms existing methods, setting a new state-of-the-art benchmark for robust and efficient 4D asset generation. The code will be made publicly available upon acceptance.

Related papers

• AnimaSpark: A Feed-Forward Method for Animating Arbitrary 3D Objects [9.201202105097991]
  Current methods for category-agnostic animation generation exhibit critical limitations in inference speed, motion quality, and adherence to textual prompts.<n>This paper introduces AnimaSpark, a novel pipeline for category-agnostic 3D animation generation.<n>Our method achieves superior performance over existing state-of-the-art techniques across key metrics, including text-motion alignment, quality of motion, and computational efficiency.
  arXiv  Detail & Related papers  (2026-06-09T15:25:39Z)
• Rigel3D: Rig-aware Latents for Animation-Ready 3D Asset Generation [6.821657791615377]
  Rigel3D is a generative method for animation-ready 3D assets represented as rigged meshes.<n>A rig-aware autoencoder decodes these representations into mesh geometry, skeleton topology, joint coordinates, and skinning weights.<n>A two-stage latent generative model synthesizes both surface and skeleton representations for image-conditioned generation.
  arXiv  Detail & Related papers  (2026-05-13T07:55:29Z)
• 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)
• Motion4D: Learning 3D-Consistent Motion and Semantics for 4D Scene Understanding [54.859943475818234]
  We present Motion4D, a novel framework that integrates 2D priors from foundation models into a unified 4D Gaussian Splatting representation.<n>Our method features a two-part iterative optimization framework: 1) Sequential optimization, which updates motion and semantic fields in consecutive stages to maintain local consistency, and 2) Global optimization, which jointly refines all attributes for long-term coherence.<n>Our method significantly outperforms both 2D foundation models and existing 3D-based approaches across diverse scene understanding tasks, including point-based tracking, video object segmentation, and novel view synthesis.
  arXiv  Detail & Related papers  (2025-12-03T09:32:56Z)
• Puppeteer: Rig and Animate Your 3D Models [105.11046762553121]
  Puppeteer is a comprehensive framework that addresses both automatic rigging and animation for diverse 3D objects.<n>Our system first predicts plausible skeletal structures via an auto-regressive transformer.<n>It then infers skinning weights via an attention-based architecture.
  arXiv  Detail & Related papers  (2025-08-14T17:59:31Z)
• MagicArticulate: Make Your 3D Models Articulation-Ready [109.35703811628045]
  We present MagicArticulate, an effective framework that automatically transforms static 3D models into articulation-ready assets.<n>Our key contributions are threefold. First, we introduce Articulation-averse benchmark containing over 33k 3D models with high-quality articulation annotations, carefully curated from XL-XL.<n>Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories.
  arXiv  Detail & Related papers  (2025-02-17T18:53:27Z)
• Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance [25.346255905155424]
  We introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework. By representing the 3D human parametric model as the motion guidance, we can perform parametric shape alignment of the human body between the reference image and the source video motion. Our approach also exhibits superior generalization capabilities on the proposed in-the-wild dataset.
  arXiv  Detail & Related papers  (2024-03-21T18:52:58Z)
• Real-time Deep Dynamic Characters [95.5592405831368]
  We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance. We use a novel graph convolutional network architecture to enable motion-dependent deformation learning of body and clothing. We show that our model creates motion-dependent surface deformations, physically plausible dynamic clothing deformations, as well as video-realistic surface textures at a much higher level of detail than previous state of the art approaches.
  arXiv  Detail & Related papers  (2021-05-04T23:28:55Z)

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.