SkelGen4D: Weakly-Supervised Skeleton-Based 4D Generation for Text-Driven Mesh Animation

2026-07-09 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Gaming & Interactive Media · Depth: Expert, quick

Summary

SkelGen4D is a weakly-supervised feed-forward framework designed for text-driven mesh animation, generating explicit skeleton motions without requiring per-frame skeleton annotations. It addresses 4D generation to synthesize temporally coherent 3D geometry sequences for animation and content creation. Unlike existing SDS-based optimization or video-driven animation, SkelGen4D uses a skeleton-driven animation framework, aligning with standard industrial pipelines for explicit control. The method first recovers temporally consistent pseudo-skeletons from animated meshes via differentiable fitting, then generates text-conditioned skeleton motion sequences, further refined with Motion-GRPO to ensure temporal coherence and physical plausibility. Evaluated on Truebones Zoo and Diffusion4D, SkelGen4D's weakly supervised approach matches or surpasses fully supervised baselines, scaling to diverse object categories for high-quality animation and supporting flexible motion editing.

Key takeaway

For 3D animators or content creators seeking efficient text-driven animation, SkelGen4D offers a significant advancement. You can now generate high-quality, temporally coherent mesh animations from text prompts without needing per-frame skeleton annotations, streamlining your workflow. Its alignment with standard industrial pipelines means you can integrate this directly, enabling flexible motion editing and scaling to diverse object categories, potentially reducing production time and costs.

Key insights

SkelGen4D enables high-quality, text-driven mesh animation using weakly-supervised skeleton generation, aligning with industry pipelines.

Principles

• Skeleton-driven animation offers explicit control.
• Weak supervision can match fully supervised baselines.
• Differentiable fitting recovers consistent pseudo-skeletons.

Method

SkelGen4D recovers pseudo-skeletons via differentiable fitting, then generates text-conditioned motion sequences, refined by Motion-GRPO for coherence and physical plausibility.

In practice

• Generate diverse text-driven mesh animations.
• Integrate into standard animation pipelines.
• Edit motions flexibly post-generation.

Topics

• 4D Generation
• Skeleton Animation
• Weakly-Supervised Learning
• Text-Driven Animation
• Mesh Animation
• Motion-GRPO

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Creative Technologist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.