# KV-Control: Parameter-Efficient K/V Injection for Trajectory-Controlled Text-to-Motion

> KV-Control injects geometric constraints as memory into the key-value pairs of self-attention layers, enabling precise trajectory control (achieving sub-centimeter precision) without modifying the main body of pre-trained text-to-motion models. It provides a lightweight control interface for animation and embodied intelligence applications.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-04T02:50:20.000Z
- 最近活动: 2026-06-05T11:54:25.757Z
- 热度: 117.9
- 关键词: text-to-motion, trajectory control, KV injection, attention mechanism, PartVQ, parameter-efficient, 3D human motion, transformer adapter
- 页面链接: https://www.zingnex.cn/en/forum/thread/kv-control-k-v
- Canonical: https://www.zingnex.cn/forum/thread/kv-control-k-v
- Markdown 来源: floors_fallback

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## KV-Control: A Lightweight Trajectory Control Method for Text-to-Motion

KV-Control is a parameter-efficient method for trajectory-controlled text-to-motion generation. It injects geometric constraints as memory into the key-value pairs of self-attention layers, enabling precise trajectory control (sub-cm precision) without modifying the main body of pre-trained text-to-motion models. This provides a lightweight control interface for applications like animation and embodied intelligence.

**Source Info**: 
- Original authors: arXiv author team
- Source platform: arXiv
- Original title: KV-Control: Parameter-Efficient K/V Injection for Trajectory-Controlled Text-to-Motion
- Link: http://arxiv.org/abs/2606.05624v1
- Release time: 2026-06-04

## Background: Control Dilemma in Text-to-Motion Generation

Text-driven 3D human motion generation models can synthesize reasonable actions from descriptive prompts, but real-world applications require precise trajectory control (e.g., root path, end effector targets) while preserving text-based action quality. This creates a trade-off between **precision** (meeting geometric constraints) and **preservation** (retaining pre-trained text-conditioned action knowledge).

Existing solutions have limitations: 
- Large-scale modification schemes: Copy generator structures for layer control access, leading to parameter redundancy and high training costs.
- Test-time optimization schemes: Shift computation to inference, sacrificing real-time efficiency.

## Method: Core Mechanism & Supporting Designs of KV-Control

KV-Control is a compact attention-side control interface for frozen text-to-motion Transformers. Its core innovation is injecting geometric constraints as 'memory' into self-attention layers instead of using global tokens or output constraints.

### Key Components:
1. **KV Injection**: Inject control conditions into key/value pairs of each self-attention layer, keeping pre-trained query streams, text cross-attention, FFNs, and main network weights frozen.
2. **PartVQ**: Anatomically aligned part codebook that decomposes actions into semantic body parts for fine-grained control, interpretability, and compression.
3. **T-Concat**: Exposes frame-part tokens as attention-addressable sites for precise control over specific time steps and body parts.

### Parameter Efficiency:
Only shared trajectory encoders and lightweight KV injection adapters are trainable, minimizing training overhead.

## Performance: Balancing Precision & Text Condition Quality

KV-Control achieves a balance between trajectory precision and text-conditioned action quality:

### Trajectory Tracking Precision:
- Root trajectory tracking: Sub-cm level accuracy.
- Multi-joint constraints: Meets multiple joint trajectory requirements.
- Time consistency: Maintains temporal coherence of actions.

### Text Condition Quality Preservation:
- Semantic consistency with text descriptions.
- Retains high-level features like gait and style.
- Preserves naturalness and fluency of actions.

## Application Scenarios of KV-Control

KV-Control's lightweight and precise control makes it suitable for:
1. **Animation Production**: Adjust specific details (e.g., character path, hand position) without re-generating the entire action.
2. **Embodied Intelligence & Robotics**: Apply to obstacle avoidance, precise end effector operations, and multi-constraint task execution.
3. **Game Development**: Enable character movement along specific paths, precise interaction with environment objects, and style-consistent actions for level design.

## Limitations & Future Research Directions

### Current Limitations:
- Focuses only on geometric trajectory constraints; other constraints (physical, social) need exploration.
- Generalization to unseen action types requires further verification.
- Extending to multi-agent interaction scenarios is a challenge.

### Future Directions:
- Explore other types of constraints (physical, social).
- Improve generalization to unseen actions.
- Extend to multi-agent scenarios.
- Apply the KV injection idea to other generative tasks (e.g., image layout control, speech prosody control).

## Conclusion: Value & Potential of KV-Control

KV-Control redefines trajectory control as a lightweight memory retrieval problem, providing a small, precise, and transparent control interface for text-to-motion generation. Its 'frozen main network + lightweight adapter' paradigm balances precise control with pre-trained model capabilities, offering a general solution for generative model control. As embodied intelligence and virtual character applications grow, this technology will become increasingly important for flexible switching between semantic description and precise control.