VideoComposer: Compositional Video Synthesis with Motion Controllability (2306.02018v2)

Abstract: The pursuit of controllability as a higher standard of visual content creation has yielded remarkable progress in customizable image synthesis. However, achieving controllable video synthesis remains challenging due to the large variation of temporal dynamics and the requirement of cross-frame temporal consistency. Based on the paradigm of compositional generation, this work presents VideoComposer that allows users to flexibly compose a video with textual conditions, spatial conditions, and more importantly temporal conditions. Specifically, considering the characteristic of video data, we introduce the motion vector from compressed videos as an explicit control signal to provide guidance regarding temporal dynamics. In addition, we develop a Spatio-Temporal Condition encoder (STC-encoder) that serves as a unified interface to effectively incorporate the spatial and temporal relations of sequential inputs, with which the model could make better use of temporal conditions and hence achieve higher inter-frame consistency. Extensive experimental results suggest that VideoComposer is able to control the spatial and temporal patterns simultaneously within a synthesized video in various forms, such as text description, sketch sequence, reference video, or even simply hand-crafted motions. The code and models will be publicly available at https://videocomposer.github.io.

• The paper presents a novel diffusion-based framework that decomposes video synthesis into textual, spatial, and temporal conditions for enhanced motion control.
• The method utilizes a Spatio-Temporal Condition encoder with cross-frame attention to improve inter-frame consistency and reduce motion error.
• Practical applications include video-to-video translation and inpainting, while limitations involve watermarked training data and resolution constraints.

Compositional Video Synthesis with Motion Controllability: An Overview of VideoComposer

The paper presents VideoComposer, a framework for compositional video synthesis with a focus on enhanced motion controllability. Leveraging recent advancements in diffusion models, VideoComposer introduces a novel approach to achieving fine-grained control over both spatial and temporal aspects of video generation.

The primary innovation lies in the decomposition of video data into three distinct but interrelated conditions: textual, spatial, and, critically, temporal. Each condition plays a pivotal role in guiding the synthesis process. For instance, textual descriptions offer high-level content instructions, whereas spatial conditions like single images or sketches provide structural guidance. Temporal conditions, particularly motion vectors extracted from compressed video data, afford precise control over the inter-frame dynamics.

A core component of VideoComposer is the Spatio-Temporal Condition encoder (STC-encoder). This architecture effectively integrates spatial and temporal dependencies through cross-frame attention mechanisms, significantly boosting inter-frame consistency. By utilizing three-dimensional UNet-based latent diffusion models (LDMs) adapted for video, VideoComposer mitigates the computational load typically associated with high-resolution video processing, thus enabling scalable application scenarios.

Qualitative and quantitative analyses reinforce the framework's efficacy. Incorporating motion vectors as temporal conditions demonstrably enhances motion controllability, reducing end-point-error in synthesized videos—a key metric for evaluating adherence to desired motion patterns. Similarly, frame consistency measures substantiate the STC-encoder's contribution to maintaining temporal cohesion across sequences.

The potential applications of VideoComposer are expansive, ranging from advanced video-to-video translation tasks to intricate video inpainting operations. The capacity to imbue synthetic videos with motions derived from hand-crafted strokes signifies a substantial leap toward customizability, facilitating user-driven creative processes. This feature circumvents limitations observed in frameworks like CogVideo, which rely heavily on textual control.

The research implications are twofold. Theoretically, VideoComposer exemplifies the virtues of compositional paradigms in generative models, advocating for further exploration into modular condition integration. Practically, the framework provides an adaptable template for real-world content creation, particularly in domains requiring nuanced motion design.

However, the paper acknowledges several limitations. The presence of watermarked data in the training process affects visual quality, while the resolution constraints hinder detail clarity. Addressing these concerns could involve integrating super-resolution techniques into the video synthesis pipeline.

In summation, VideoComposer offers a promising trajectory for future developments in AI-driven video synthesis. By coupling explicit motion control signals with diffusion-based models, it opens pathways to more sophisticated and controllable generative systems. Further research might explore broader datasets and enhanced architectural designs to transcend current visual fidelity barriers.