Uni3C: Unifying Precisely 3D-Enhanced Camera and Human Motion Controls for Video Generation (2504.14899v1)

Abstract: Camera and human motion controls have been extensively studied for video generation, but existing approaches typically address them separately, suffering from limited data with high-quality annotations for both aspects. To overcome this, we present Uni3C, a unified 3D-enhanced framework for precise control of both camera and human motion in video generation. Uni3C includes two key contributions. First, we propose a plug-and-play control module trained with a frozen video generative backbone, PCDController, which utilizes unprojected point clouds from monocular depth to achieve accurate camera control. By leveraging the strong 3D priors of point clouds and the powerful capacities of video foundational models, PCDController shows impressive generalization, performing well regardless of whether the inference backbone is frozen or fine-tuned. This flexibility enables different modules of Uni3C to be trained in specific domains, i.e., either camera control or human motion control, reducing the dependency on jointly annotated data. Second, we propose a jointly aligned 3D world guidance for the inference phase that seamlessly integrates both scenic point clouds and SMPL-X characters to unify the control signals for camera and human motion, respectively. Extensive experiments confirm that PCDController enjoys strong robustness in driving camera motion for fine-tuned backbones of video generation. Uni3C substantially outperforms competitors in both camera controllability and human motion quality. Additionally, we collect tailored validation sets featuring challenging camera movements and human actions to validate the effectiveness of our method.

Uni3C: A Unified Framework for 3D-Enhanced Video Generation

The paper "Uni3C: Unifying Precisely 3D-Enhanced Camera and Human Motion Controls for Video Generation" presents a novel framework to address the challenges of controllable video generation. Focusing on integrating camera and human motion controls, the authors introduce Uni3C, a system designed to circumvent the limitations of existing approaches that typically handle these aspects discretely.

Key Contributions

Uni3C's primary innovation lies in its ability to manage both camera and human motion controls through a single, unified framework. The paper introduces two core contributions facilitating this unification:

1. PCDController: The cornerstone of the Uni3C framework is the PCDController, a plug-and-play module designed to work with a frozen video generative backbone. By leveraging unprojected 3D point clouds from monocular depth, PCDController enables precise camera control. The remarkable adaptability of this module is manifested in its generalization capabilities, whether the backbone is frozen or fine-tuned. Furthermore, the design allows for domain-specific training, where modules focusing on different aspects, such as camera control or human motion, can be developed independently, mitigating the requirement for extensively labeled datasets.
2. Unified 3D World Guidance: The framework also introduces globally aligned 3D world guidance, seamlessly integrating scenic point clouds with SMPL-X characters. This alignment ensures coherent control signals for both camera and human movements during inference, significantly improving the quality and realism of the generated video content.

Experimental Validation

Extensive experiments demonstrate Uni3C's substantial performance improvements over existing methods in both camera controllability and human motion realism. The authors apply a robust evaluation strategy, employing challenging camera movements and human action scenarios to validate their framework. The Uni3C framework consistently outperforms its peers, confirming the effectiveness of its modular and unified approach.

Implications and Future Directions

By addressing the interdependency of camera and human motion control through a unified framework, Uni3C has significant implications for applications across virtual reality, interactive media, and film production. The ability to produce videos with precise control over both camera angles and human motion opens avenues for creating immersive and engaging content. Theoretically, Uni3C can serve as a robust platform for exploring more advanced video generation techniques, potentially incorporating aspects of physics-informed motion synthesis and real-time video editing.

Looking forward, an exciting area of exploration would be the extension of Uni3C's principles to broader domains and applications within AI and computer graphics, potentially leveraging advancements in neural rendering and real-time motion capture technologies. Further research could also explore the integration of additional modalities, such as lighting control and environmental physics, to push the boundaries of holistic video generation even further.

In conclusion, Uni3C represents a significant stride in the evolution of controllable video generation systems, providing a scalable and flexible framework that effectively addresses the intertwined challenges of camera and human motion control in digital content creation. The framework's design principles could inform future developments in AI-driven video synthesis, heralding a new era of creativity and precision in the field.