We study 4D generation to synthesize temporally coherent sequences of 3D geometry for animation and content creation. In contrast to existing SDS-based optimization methods and video-driven animation approaches, we adopt a skeleton-driven animation framework aligned with standard industrial pipelines, which enables explicit control and editing.
The paper presents a new approach to automatic skeleton generation that tackles two key challenges: encoding branch structures into token sequences and leveraging test-time computation. It introduces branch‑centric tokenization, a representation that groups structurally related elements together and directly encodes connectivity, producing more compact sequences than traditional BFS serialization. Additionally, the authors propose view‑augmented generation, a test‑time augmentation technique that rotates the input mesh, aligns predictions, and selects the best skeleton based on mesh coverage and consistency across views, leading to improved accuracy on benchmark datasets.
arXiv:2608.31113v1 Announce Type: new Abstract: We introduce BLARM, a feed-forward method for video-driven 3D mesh animation. Given a monocular video and a static object mesh, BLARM predicts a tempor...
Rigging is inherently task-dependent because the same mesh may require different skeletons and deformation behaviors across animation tasks. In practice, artists often inspect an initial rig and repeatedly edit its skeletal structure and deformation behavior to meet specific animation requirements.
arXiv:2604.26917v2 Announce Type: replace Abstract: Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due...
arXiv:2606. 28215v1 Announce Type: cross Abstract: Extracting dynamic 4D object interactions from massive, in-the-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs.
arXiv:2610.28632v1 Announce Type: cross Abstract: Video generation models can now depict remarkably realistic motion. Yet it is unclear how to use this motion beyond pixels: to animate a specific 3D...
arXiv:2607. 08725v1 Announce Type: cross Abstract: Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable.
arXiv:2605. 13838v3 Announce Type: replace-cross Abstract: Video-guided 3D animation holds immense potential for content creation, offering intuitive and precise control over dynamic assets.
arXiv:2611.02902v2 Announce Type: replace Abstract: Humanoid rigging for modern characters with complex garments remains challenging due to the need for dense auxiliary skeletons and precise skinning...
arXiv:2607. 10984v1 Announce Type: cross Abstract: Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding the skeleton kinematics, severely limiting generalization, preventing cross-dataset training, and requiring complex data retargeting.
arXiv:2606.10142v2 Announce Type: replace Abstract: Recent advances in 3D generation have led to substantial improvements in realism, controllability, and efficiency, yet the evaluation of 3D assets...