A Unified Conditional Flow for Motion Generation, Editing, and Intra-Structural Retargeting

2017-09-28 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Graphics & Animation · Depth: Expert, extended

Summary

A new unified conditional flow model addresses the traditional fragmentation between text-driven motion editing and intra-structural retargeting, where characters share topology but differ in bone lengths. This model, built on rectified flow and a DiT-style transformer, casts both tasks as conditional transport problems, distinguishing them only by the modulated conditioning signal (semantic or structural). The architecture uses per-joint tokenization and explicit joint self-attention to enforce kinematic dependencies, alongside a multi-condition classifier-free guidance strategy for balancing text adherence and skeletal conformity. Evaluated on SnapMoGen and a multi-character Mixamo subset, the single trained model supports text-to-motion generation, zero-shot editing, and zero-shot intra-structural retargeting, simplifying deployment and improving structural consistency compared to task-specific baselines.

Key takeaway

For research scientists developing character animation tools, this unified conditional flow model offers a streamlined approach to motion manipulation. You can now perform text-driven editing and intra-structural retargeting with a single model, eliminating the need for fragmented pipelines and improving structural consistency. Consider integrating dual-conditioning architectures to simplify your development workflows and enhance the versatility of your generative models.

Key insights

Motion editing and retargeting are unified as conditional transport tasks within a single generative flow model.

Principles

• Modulate conditioning signals to achieve diverse generative tasks.
• Explicitly model kinematic dependencies for structural consistency.

Method

A rectified-flow motion model is jointly conditioned on text prompts and target skeletal structures, using a DiT-style transformer with per-joint tokenization and joint self-attention for unified generation, editing, and retargeting.

In practice

• Use a single model for motion generation, editing, and retargeting.
• Employ per-joint tokens for detailed body-part level control.

Topics

• Unified Conditional Flow
• Motion Generation
• Motion Editing
• Intra-Structural Retargeting
• DiT-style Transformer Architecture

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.