OmniControl: Control Any Joint at Any Time for Human Motion Generation (2310.08580v2)

Abstract: We present a novel approach named OmniControl for incorporating flexible spatial control signals into a text-conditioned human motion generation model based on the diffusion process. Unlike previous methods that can only control the pelvis trajectory, OmniControl can incorporate flexible spatial control signals over different joints at different times with only one model. Specifically, we propose analytic spatial guidance that ensures the generated motion can tightly conform to the input control signals. At the same time, realism guidance is introduced to refine all the joints to generate more coherent motion. Both the spatial and realism guidance are essential and they are highly complementary for balancing control accuracy and motion realism. By combining them, OmniControl generates motions that are realistic, coherent, and consistent with the spatial constraints. Experiments on HumanML3D and KIT-ML datasets show that OmniControl not only achieves significant improvement over state-of-the-art methods on pelvis control but also shows promising results when incorporating the constraints over other joints.

• The paper introduces OmniControl, a novel diffusion-based model that employs spatial and realism guidance to control any joint in human motion generation.
• It utilizes a hybrid guidance mechanism to transform motions into global coordinates and significantly reduces pelvis control error by 79.2% compared to prior methods.
• Empirical evaluations on benchmarks like HumanML3D and KIT-ML demonstrate its superior performance in achieving realistic, multi-joint coordination over state-of-the-art approaches.

Overview of "OmniControl: Control Any Joint at Any Time for Human Motion Generation"

The paper introduces a novel approach, OmniControl, which advances the field of human motion generation by allowing precise control over any joint at any time within a diffusion-based motion model. Unlike existing methods that predominantly focus on controlling the pelvis trajectory, OmniControl facilitates the incorporation of flexible spatial control signals across multiple joints. This is achieved via the twin mechanisms of spatial and realism guidance that are designed to optimize the trade-off between control accuracy and motion realism.

Theoretical and Practical Contributions

1. Novel Control Approach: OmniControl is distinctively positioned as the premier approach to offer broad spatial control over multiple joints, standing in contrast to established methods that predominantly target the pelvis. The introduction of both spatial and realism guidance ensures a balance between adherence to control signals and the overall realism of generated poses.
2. Hybrid Guidance Mechanisms: The spatial guidance transforms generated motion into global coordinates to directly compare with input control signals, ensuring high accuracy in joint placement. Realism guidance, inspired by techniques in controllable image generation, ensures coherence across all joints and enhances the naturalism of motion sequences.
3. Implementation Power and Flexibility: Empirical results reinforce the efficacy of OmniControl, demonstrating superior performance against state-of-the-art methodologies like MDM, PriorMDM, and GMD in both realism and control accuracy, particularly in pelvis control. More impressively, it shows promising results across varied scenarios, where multiple joints require coordinated control.

Empirical Evaluation and Results

Extensive experimentation is carried out using benchmark datasets such as HumanML3D and KIT-ML. The results on these datasets indicate that OmniControl not only sets new standards in pelvis control but also achieves competent performance in controlling diverse joints. Key metrics such as Fréchet Inception Distance (FID), R-Precision, and Diversity illustrate OmniControl's competitive advantage in producing realistic, semantically coherent motions.

In scenarios requiring sparse control signals, OmniControl performs robustly, contrasting sharply with its predecessors which exhibit limited flexibility. This capability is crucial for applications necessitating highly specific joint trajectories within larger, complex motion sequences.

The paper presents compelling numerical evidence of OmniControl's prowess. For instance, it substantially reduces Avg. err. by 79.2% in pelvis control when benchmarked against GMD. Such numerical advancements underscore the utility of OmniControl in practical applications, including, but not limited to, human-object interactions and navigation tasks in constrained environments.

Implications and Future Directions

OmniControl's contributions extend beyond immediate empirical successes. The architecture's flexibility and robustness pave the way for enriched AI-driven animation and simulation applications in virtual reality (VR), gaming, and robotic motion planning. By improving control precision across multi-joint setups, the technique can simulate realistic human interactions within digital spaces, enhancing user experience and functional outputs in these domains.

Furthermore, future work could investigate incorporating OmniControl within real-time motion generation systems, optimizing computational efficiency and responsiveness to dynamic control requirements. Integrating physics-based constraints could further advance the realism and applicability of generated motions, reducing artifacts like foot skating observed in some synthesized sequences.

In conclusion, "OmniControl: Control Any Joint at Any Time for Human Motion Generation" offers a significant methodological innovation within human motion synthesis, bridging the gap between theoretical modeling and practical demands of control flexibility and motion naturalism. Its twin-guidance mechanism lays a foundational framework for next-generation AI systems that demand precise and adaptable control over human-like movements in complex operational settings.