Motion Control for Enhanced Complex Action Video Generation
The paper presents MVideo, a novel text-to-video (T2V) model framework crafted to address inherent challenges in generating complex action videos, a task that has proven difficult for existing models. Standard T2V models often struggle to effectively depict intricate motions due to the limited expressive power of text prompts. MVideo introduces a breakthrough by incorporating mask sequences as an additional conditioning input, enhancing action precision and fluidity in longer-duration videos.
MVideo Framework and Methodology
MVideo leverages advanced vision models such as GroundingDINO and SAM2 to automatically generate mask sequences for objects in video frames. This additional input serves to create a more defined depiction of motion, transcending the limitations of text-based prompts. The model employs a unique dual control mechanism, allowing users to independently or jointly adjust text prompts and motion conditions, paving the way for more dynamic video generation.
The framework is engineered to maintain temporal coherence across extended video durations, which is essential for rendering coherent action sequences. MVideo employs an efficient iterative video generation approach, synergizing image conditions with low-resolution video conditions, thereby reducing computational overhead while preserving temporal consistency.
Training and Evaluation
The model is fine-tuned from the CogvideoX video diffusion model by integrating mask sequence conditions, backed by a novel consistency loss to mitigate declines in text alignment. This ensures that the model retains its original ability to align text prompts while still learning mask sequence alignment. Evaluation metrics compare MVideo with state-of-the-art models like OpenSora and CogvideoX, highlighting MVideo's superior performance in generating videos with more intricate actions and high mask mIoU scores, demonstrating its efficacy in scenarios requiring complex motion depiction.
Several ablation studies reveal the importance of the consistency loss in balancing text and mask sequence alignment accuracy. Furthermore, the model shows robust generalization capabilities, performing well on unseen masks and complex motion scenarios not seen during training.
Implications and Future Directions
MVideo advances the state of the art in video diffusion models by proposing a structured way to incorporate motion conditions via a mask sequence. This work opens new avenues for customizing video content beyond the constraints of text-based prompts, allowing for nuanced control over both object and scene dynamics. Practically, the enhanced action depiction capability has applications ranging from film production to virtual reality environments where a high degree of motion specificity is required.
Future research could extend MVideo's capabilities to other domains, expanding the range of mask sequences and integrating more complex scene dynamics. Additionally, further exploration could refine the underlying mechanisms for automating mask extraction to reduce computational demands and improve efficiency. As foundational vision models continue to evolve, they may offer new opportunities for advancing the precision and applicability of models like MVideo in generating long, coherent, and dynamic video content.