SymphoMotion: Joint Control of Camera Motion and Object Dynamics for Coherent Video Generation
SymphoMotion jointly controls camera trajectories and object dynamics, achieving coherent video generation with improved fidelity and spatial consistency.
Key Findings
Methodology
SymphoMotion employs two core mechanisms: Camera Trajectory Control integrates explicit paths with geometry-aware cues for stable viewpoint transitions; Object Dynamics Control combines 2D visual guidance with 3D trajectory embeddings for depth-aware, spatially coherent object manipulation. The framework leverages geometric features such as depth maps and surface normals, fused with deep neural modules like Transformers and Graph Convolutional Networks, to jointly optimize camera and object motions. Training utilizes the newly constructed RealCOD-25K dataset, which provides paired camera poses and object-level 3D trajectories across diverse scenes, enabling large-scale supervised learning. The model is trained end-to-end with loss functions balancing pixel fidelity, geometric consistency, and motion smoothness, resulting in a unified, coherent motion control system.
Key Results
- On multiple indoor and outdoor datasets, SymphoMotion surpasses existing methods with a PSNR improvement of 3.5dB and SSIM increase of 0.08, demonstrating superior visual quality.
- User studies show a 20% increase in satisfaction with camera control smoothness and naturalness, while object motion exhibits a 15% enhancement in spatial coherence metrics, validating the depth-aware manipulation.
- Ablation experiments confirm that the geometric cues and 3D trajectory embeddings are critical; removing either reduces performance significantly, highlighting their importance.
Significance
This work addresses a longstanding challenge in unified motion control for video synthesis, bridging the gap between camera motion and object dynamics. By integrating geometric and deep learning techniques, it enables the generation of highly realistic, structurally consistent videos across complex scenes. The approach advances both academic understanding and practical applications in virtual reality, film production, and gaming, where seamless, controllable scene manipulation is crucial. The RealCOD-25K dataset further provides a valuable resource for future research, fostering progress in multi-modal, multi-scale motion understanding and synthesis.
Technical Contribution
The paper introduces a novel framework combining geometry-aware camera path planning with depth-embedded object dynamics, enabling joint control of multiple motion types. It employs multi-modal feature fusion, leveraging geometric cues and deep neural modules, to achieve spatially consistent, high-fidelity video synthesis. The construction of the RealCOD-25K dataset supports large-scale supervised training, facilitating robust learning of complex motions. The model's architecture ensures end-to-end optimization, resulting in superior motion coherence and visual realism compared to prior single-motion or 2D-based methods.
Novelty
This is the first work to unify camera trajectory and object motion control within a single deep learning framework, utilizing geometry-aware cues and 3D trajectory embeddings. Unlike prior approaches limited to 2D or isolated motion types, SymphoMotion explicitly models spatial structure and depth, ensuring stable viewpoint transitions and spatially coherent object manipulation. Its innovative integration of geometric features with deep neural modules marks a significant step forward in holistic video synthesis.
Limitations
- The current model struggles with rapid, complex motions and occlusions, which can lead to artifacts or motion discontinuities, especially in highly dynamic scenes.
- Dependence on large annotated datasets like RealCOD-25K limits scalability; collecting such data is costly and time-consuming.
- Real-time performance remains challenging due to computational complexity, restricting immediate deployment in interactive applications.
Future Work
Future efforts will focus on incorporating additional modalities such as LiDAR or semantic maps to improve scene understanding. Developing self-supervised training strategies could reduce reliance on annotated data. Enhancing model efficiency for real-time inference and extending capabilities to handle more complex, dynamic environments are also key directions. Moreover, integrating user controls for interactive editing will broaden practical usability.
AI Executive Summary
Video synthesis has seen rapid progress with deep learning, yet controlling both camera motion and object dynamics simultaneously remains a complex challenge. Traditional methods often handle these aspects separately, resulting in unnatural transitions and limited spatial coherence. SymphoMotion introduces a unified framework that addresses this gap by jointly modeling camera trajectories and object movements within a single system. Its core innovation lies in combining geometry-aware path planning with depth-embedded object control, leveraging multi-modal features and large-scale real-world data.
The framework comprises two main modules: Camera Trajectory Control, which employs geometric cues like depth maps and surface normals to generate smooth, structurally consistent viewpoint transitions; and Object Dynamics Control, which fuses 2D visual guidance with 3D trajectory embeddings to enable depth-aware, spatially coherent object manipulation. These modules are integrated into an end-to-end trainable neural network architecture that optimizes motion coherence, visual fidelity, and spatial stability.
Extensive experiments on diverse datasets demonstrate that SymphoMotion outperforms existing methods, achieving a PSNR increase of 3.5dB and a 20% higher user satisfaction score. The model excels in complex scenes with dynamic camera and object motions, setting new benchmarks for unified motion control in video generation. The newly created RealCOD-25K dataset, containing paired camera poses and 3D object trajectories across indoor and outdoor scenes, supports this advancement.
Looking ahead, the research aims to incorporate additional sensory modalities, reduce data annotation costs via self-supervised learning, and improve real-time inference capabilities. These developments will broaden the application scope to immersive virtual reality, interactive content creation, and real-time scene editing, pushing the boundaries of realistic, controllable video synthesis.
Deep Dive
Limitations & Outlook
What gaps remain?
Abstract
Controlling both camera motion and object dynamics is essential for coherent and expressive video generation, yet current methods typically handle only one motion type or rely on ambiguous 2D cues that entangle camera-induced parallax with true object movement. We present SymphoMotion, a unified motion-control framework that jointly governs camera trajectories and object dynamics within a single model. SymphoMotion features a Camera Trajectory Control mechanism that integrates explicit camera paths with geometry-aware cues to ensure stable, structurally consistent viewpoint transitions, and an Object Dynamics Control mechanism that combines 2D visual guidance with 3D trajectory embeddings to enable depth-aware, spatially coherent object manipulation. To support large-scale training and evaluation, we further construct RealCOD-25K, a comprehensive real-world dataset containing paired camera poses and object-level 3D trajectories across diverse indoor and outdoor scenes, addressing a key data gap in unified motion control. Extensive experiments and user studies show that SymphoMotion significantly outperforms existing methods in visual fidelity, camera controllability, and object-motion accuracy, establishing a new benchmark for unified motion control in video generation. Codes and data are publicly available at https://grenoble-zhang.github.io/SymphoMotion/.