Source-linked AI summary
SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonization
Lifan Wu, Ruijie Zhu, Yubo Ai, Tianzhu Zhang
TL;DR
Existing 4D representations often use implicit deformation fields that limit direct control and editability. SkeletonGaussian generates editable 4D Gaussian models from monocular video using hierarchical skeleton-driven rigid motion plus non-rigid refinement, and reports superior quality over existing methods while supporting animation-oriented motion editing.
Problem
Implicit deformation fields limit direct control and editability, require retraining for motion edits, scale quadratically with time, and do not readily convert to standard skeleton or pose data.
Method
SkeletonGaussian hierarchically decomposes motion into skeleton-driven rigid deformation and fine non-rigid deformation within a three-stage 4D Gaussian generation pipeline.
Results
SkeletonGaussian delivers superior generation quality over existing methods while enabling intuitive motion editing and alignment with standard animation workflows.
Takeaways & Limitations
Explicit skeleton poses provide interpretable motion controls that can be adjusted directly and integrated with standard animation pipelines.
Takeaways & Limitations
The method can perform poorly when objects lack a clear skeleton structure or when skeleton extraction contains severe topological errors.
Abstract
from arXiv · showhide
4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or videos. However, existing methods often represent motion as an implicit deformation field, which limits direct control and editability. To address this issue, we propose SkeletonGaussian, a novel framework for generating editable dynamic 3D Gaussians from monocular video input. Our approach introduces a hierarchical articulated representation that decomposes motion into sparse rigid motion explicitly driven by a skeleton and fine-grained non-rigid motion. Concretely, we extract a robust skeleton and drive rigid motion via linear blend skinning, followed by a hexplane-based refinement for non-rigid deformations, enhancing interpretability and editability. Experimental results demonstrate that SkeletonGaussian surpasses existing methods in generation quality while enabling intuitive motion editing, establishing a new paradigm for editable 4D generation. Project page: https://wusar.github.io/projects/skeletongaussian/
1. Introduction
SkeletonGaussian addresses the limited editability of implicit 4D motion representations with a hierarchical skeleton-driven framework that combines rigid skeletal motion and fine non-rigid refinement. It targets high-quality generation from monocular video while supporting direct, real-time motion editing and animation-pipeline integration.
- Motivation: Existing 4D methods commonly model motion with implicit deformation fields, limiting direct control, requiring retraining for edits, and scaling quadratically with sequence duration.These representations are also difficult to convert into standard skeleton or pose data for animation tools.
- Method: SkeletonGaussian introduces a lightweight hierarchical articulated representation that captures motion details across multiple levels for editable 4D generation.The framework is designed to provide efficient, high-quality generation with flexible editing capabilities.
- Method: The pipeline decomposes object motion into sparse rigid deformation driven by a skeleton and fine non-rigid deformation for details such as clothing and skin wrinkles.It uses linear blend skinning for rigid motion and a subsequent refinement stage for non-rigid effects.
- Method: SkeletonGaussian uses three stages: static 3D Gaussian generation, rigid motion modeling, and non-rigid motion refinement.UniRig serves as the default skeleton extractor for robust, category-agnostic rigging.
- Editability: The explicit skeleton representation enables direct pose-based motion editing, real-time adjustments, and export to standard skeleton and pose formats for Blender workflows.Users modify object motion by adjusting skeleton poses rather than optimizing an implicit deformation field.
- Efficiency: Skeleton-based pose parameters grow linearly with joints and time, O(B × T), making them more parameter-efficient than dense deformation fields.The paper connects this representation to reduced storage demands, training time, and memory requirements.
2. Related Work
Prior 4D methods use implicit deformation fields, while SkeletonGaussian combines established Gaussian generation and skeleton-based motion representations into an editable, hierarchical pipeline.
- Skeleton-Based Motion Representations: Skeleton-based representations provide manipulable control for detailed object motion, with LBS commonly animating surfaces through hierarchical joint transformations.Related models include SMPL for human motion, FLAME for facial animation, and SMAL for animals.
- Static 3D Gaussian and Skeleton Generation: SkeletonGaussian generates a canonical static Gaussian from the video midpoint, then extracts a skeletal structure for subsequent motion modeling.The midpoint is selected because it minimizes motion discrepancy across frames, reducing task complexity.
- Rigid and Non-Rigid Motion Modeling: The pipeline applies LBS-driven skeletal poses to produce rigid motion, then uses a deformation field to refine the result into observation-space Gaussians.The stages share training objectives, and a differentiable rasterizer compares rendered views with reference video signals.
- Generated Motion Editing: Explicit skeleton controls support direct pose editing, hierarchical propagation from parent joints to children, and integration with animation tools such as Blender.Users can modify poses at specific time steps to alter the motion trajectory.
4. Experiments
SkeletonGaussian is evaluated on Consistent4D through quantitative comparisons, qualitative renderings, user studies, and ablations of motion modeling, initialization, pose smoothness, and skeleton extraction.
- Experiment Setup: The pipeline generates six anchor-view videos, initializes 10,000 Gaussian points, trains static and skeleton stages, and refines non-rigid motion with a hexplane and deformation MLP.Training uses 1,500 static-stage steps, 2,500 skeleton-stage steps, and 7,000 refinement steps.
- Comparisons: SkeletonGaussian is compared with STAG4D and DreamGaussian4D using two viewpoints and two time steps per instance, alongside visualized skeleton poses.
- Comparisons: SkeletonGaussian outperforms STAG4D, DreamGaussian4D, and 4DGen on Consistent4D for reference-view LPIPS and CLIP, and achieves the lowest FVD.The reported FVD result indicates fewer temporal artifacts and closer matching to real-world footage.
- Comparisons: Qualitative results show high-fidelity reconstruction with stable geometry, consistent texture, and preserved fine details across frames.
- Ablation Studies: Rigid-only LBS preserves articulated structure but underfits fine non-rigid motion, while the full model combines rigid and non-rigid stages.The ablation compares CLIP, LPIPS, FVD, VRAM, and training time for deformation modules excluding the static Gaussian.
- Ablation Studies: Pose smoothness improves temporal continuity and reduces jitters, while replacing UniRig with Coverage Axis++ weakens stability and increases artifacts.UniRig also produces more stable, semantically aligned joints, improving control and reducing topological errors.
- Ablation Studies: Selecting a clear-visibility, neutral-pose frame improves canonical initialization, but the skeleton-driven mechanism remains relatively robust to initial-frame choice.Table 3 compares Frame 0, Frame 15, and a random frame, reporting consistent performance across initializations.
5. Discussion
The discussion identifies skeleton quality and object structure as important boundaries for SkeletonGaussian, while outlining extensions for multi-object motion and skeleton initialization.
- Limitations and Future Directions: Incorrect or topologically erroneous skeleton retrieval can degrade generated results, especially for objects without a clear skeleton structure.The hexplane deformation field can compensate for mild skeletal inaccuracies, but severe extraction errors remain problematic.
- Limitations and Future Directions: SkeletonGaussian currently does not support multi-object motion, limiting applicability to scenarios involving multiple objects.The authors suggest independent skeletons for each object as a future direction.
- Limitations and Future Directions: Future extensions include predefined skeleton templates such as SMPL and ViTPose-based skeleton-pose initialization.These integrations are intended to improve the accuracy and quality of 4D motion generation.
6. Conclusion
SkeletonGaussian generates editable 4D Gaussian models from monocular video by separating rigid skeletal motion from fine-grained non-rigid deformation. Its hierarchical design supports motion control through skeleton poses and achieves superior quality over existing methods.
- SkeletonGaussian generates editable 4D Gaussian-based models from monocular video through a three-phase hierarchical framework.The phases construct a static 3D Gaussian model, model rigid motion with skeletal linear blend skinning, and refine non-rigid motion with a hexplane deformation field.
- The framework explicitly decomposes motion into rigid skeletal movements and fine-grained non-rigid details.This decomposition improves control and interpretability in 4D Gaussian modeling.
- Adjusting skeleton poses enables intuitive motion editing and alignment with standard animation workflows.
- SkeletonGaussian delivers superior quality over existing methods in experimental evaluation.
7. HexPlane Deformation Field
The HexPlane-based deformation model refines rigidly deformed Gaussians toward observed Gaussians by predicting per-Gaussian positional, rotational, and scaling changes from space-time coordinates.
- The HexPlane-plus-MLP model refines the rigidly deformed Gaussian Gr into the observed Gaussian Go.
- It estimates each Gaussian’s positional offset, rotational variation, and scaling adjustment from spatial coordinates (x, y, z) and time t.
- HexPlane decomposes the 4D field into six feature planes, each corresponding to a pair of coordinate axes.
8. Implementation Details
Implementation initializes and trains a static Gaussian object, extracts a 70-point skeleton, then trains temporally smoothed skeleton poses with a decaying learning rate.
- The static object is initialized with 10000 Gaussian points inside a sphere of radius 2 and trained for 1500 steps.
- Coverage Axis++ extracts 70 skeleton points from the trained static 3D Gaussian.
- Skeleton poses are trained for 2,500 steps with a smoothing window of size 3 for temporal smoothness.
- The pose-training learning rate decreases from 0.00005 to 0.000005.
9. Additional Information on Loss Functions
The loss formulation combines multi-view SDS with reconstruction, foreground-mask, and total-variation terms to optimize 4D Gaussian representations while promoting consistency.
- Multi-view SDS uses generated images and reference images, selecting the reference view closest to the rendered camera view for SDS computation.The formulation uses weighting parameters α1 and α2 for the respective SDS terms.
- The SDS gradient optimizes 3D representation parameters from rendered images, diffusion timesteps, and predicted versus ground-truth noise.The predicted noise is conditioned on the initial input and relative camera pose (R, T).
- Reconstruction and foreground-mask losses compare generated and reference images and their corresponding foreground masks.
- Total Variation regularization is applied to ensure spatiotemporal consistency.
- The final objective weights the respective loss terms using λ1, λ2, and λ3.
10. Additional Results for 4D Generation
SkeletonGaussian’s additional visualizations show that its extracted skeleton poses align with the generated objects’ movements across rotation views and front views.
- 10. Additional Results for 4D Generation: Skeleton poses align seamlessly with the objects’ movements, highlighting SkeletonGaussian’s motion-modeling ability.The results include rotation-view and front-view visualizations of generated objects alongside their corresponding skeletons.
11. Failure Cases
The reported failures arise mainly from inaccurate skeleton extraction and the mismatch between skeletal representations and non-articulated structures. Extraction errors can be mitigated through canonical-frame selection or manual refinement, whereas skeletal modeling struggles with rigid sliding motions.
- 11. Failure Cases: Skeleton extraction quality significantly impacts 4D generation performance, although the reported failures occur infrequently in testing.The investigation identifies inaccurate skeleton extraction and non-skeletal structures as two primary failure categories.
- Category 1: Inaccurate Skeleton Extraction: In the egret case, crossed legs in the canonical 15th frame caused incorrect skeletal connections and degraded generation quality.Selecting the 10th frame instead or manually refining the extracted skeleton can mitigate this issue.
- Category 2: Non-Skeletal Structures: Skeletal parameterization cannot accurately represent pistol-barrel sliding motion because the barrel lacks articulation.The limitation concerns rigid translations along the barrel axis, which skeletal transformations cannot effectively approximate.
- Category 2: Non-Skeletal Structures: Skeletal approaches are better suited to naturally articulated humans, animals, and plants, where motion priors align with the skeletal representation space.This scope boundary follows the authors’ contrast between the pistol failure and naturally articulated objects.