FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance (2408.08189v2)

Abstract: Synthesizing motion-rich and temporally consistent videos remains a challenge in artificial intelligence, especially when dealing with extended durations. Existing text-to-video (T2V) models commonly employ spatial cross-attention for text control, equivalently guiding different frame generations without frame-specific textual guidance. Thus, the model's capacity to comprehend the temporal logic conveyed in prompts and generate videos with coherent motion is restricted. To tackle this limitation, we introduce FancyVideo, an innovative video generator that improves the existing text-control mechanism with the well-designed Cross-frame Textual Guidance Module (CTGM). Specifically, CTGM incorporates the Temporal Information Injector (TII), Temporal Affinity Refiner (TAR), and Temporal Feature Booster (TFB) at the beginning, middle, and end of cross-attention, respectively, to achieve frame-specific textual guidance. Firstly, TII injects frame-specific information from latent features into text conditions, thereby obtaining cross-frame textual conditions. Then, TAR refines the correlation matrix between cross-frame textual conditions and latent features along the time dimension. Lastly, TFB boosts the temporal consistency of latent features. Extensive experiments comprising both quantitative and qualitative evaluations demonstrate the effectiveness of FancyVideo. Our video demo, code and model are available at https://360cvgroup.github.io/FancyVideo/.

An Expert Analysis of "FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance"

The paper "FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance" investigates the challenges and proposes solutions for generating temporally consistent and motion-rich videos from textual prompts. Unlike existing text-to-video (T2V) models that often suffer from inadequate time-specific textual guidance, leading to inconsistent and static videos, FancyVideo introduces a novel approach that enhances the textual guidance mechanism using the Cross-frame Textual Guidance Module (CTGM).

Overview of FancyVideo

FancyVideo directly addresses the limitations of existing T2V models, which largely rely on spatial cross-attention for text control without incorporating frame-specific textual guidance. This kind of guidance is crucial for maintaining temporal consistency and ensuring the smooth transition of motion across frames. To this end, FancyVideo leverages CTGM, composed of three key components:

1. Temporal Information Injector (TII)
2. Temporal Affinity Refiner (TAR)
3. Temporal Feature Booster (TFB)

These components strategically modify the cross-attention mechanism to inject and refine temporal information, ensuring that the motion and spatial-temporal consistency are accurately interpreted from the text prompts.

Contribution and Architecture

FancyVideo makes several noteworthy contributions to the field of video generation:

1. Novel Textual Guidance Mechanism: By implementing CTGM, FancyVideo provides a fresh perspective on text control in video generation, emphasizing the importance of cross-frame textual conditions.
2. Enhancing Temporal Consistency: The CTGM addresses the inherent challenges in synchronizing temporal aspects across frames, significantly improving motion consistency.
3. Superior Performance: Extensive experiments demonstrate that FancyVideo attains state-of-the-art (SOTA) results on established benchmarks like EvalCrafter, UCF-101, and MSR-VTT, as well as in human evaluations.

The model architecture comprises a pseudo-3D UNet structure that combines spatial blocks from a pre-trained T2I model with CTGM and temporal attention blocks. This integration ensures the effective handling of input features such as noisy latent, mask indicator, and image indicator to generate temporally consistent video frames.

Experimental Evaluation and Results

EvalCrafter Benchmark:

FancyVideo exhibited strong performance across multiple metrics detailed in the EvalCrafter benchmark, outperforming existing methods in several key dimensions:

• Video Quality: Achieved superior scores in VQAA and VQAT.
• Text-Video Alignment: Demonstrated excellent performance in metrics like CLIP-Score, BLIP-BLEU, and SD-Score.
• Motion Quality: Maintained competitive scores, second only to Show-1 but with significantly better video quality.
• Temporal Consistency: Achieved high scores across metrics like CLIP-Temp and Face Consistency.

UCF-101 and MSR-VTT:

The model demonstrated competitive results with notable scores in FVD, IS, FID, and CLIPSIM, confirming the robustness of FancyVideo across varied datasets and benchmarks.

Human Evaluation:

FancyVideo also performed exceptionally well in human evaluations, which considered aspects like video quality, text-video alignment, motion quality, and temporal consistency.

Implications and Future Prospects

Theoretical Implications:

The introduction of CTGM provides a crucial advancement in T2V models by illustrating the importance of temporal-specific textual guidance. This paves the way for future research to explore more sophisticated temporal alignment techniques and refine attention mechanisms further.

Practical Implications:

FancyVideo's ability to generate high-quality, temporally consistent videos can significantly impact various applications, from video content creation to virtual reality experiences. By achieving SOTA results and improving the realism of generated videos, FancyVideo can enhance user engagement and provide more authentic experiences.

Future Developments:

Further research may involve exploring adaptive CTGM mechanisms tailored for various video generation tasks. Additionally, integrating FancyVideo with real-time processing capabilities and extending its application to other domains such as automated filmmaking and interactive storytelling could be promising avenues for future exploration.

Conclusion

FancyVideo represents an important step forward in the field of text-to-video generation, addressing key challenges and setting new benchmarks in video quality and temporal consistency. The introduction of the Cross-frame Textual Guidance Module (CTGM) significantly enhances the model's capability to interpret and generate complex spatial-temporal relationships from text prompts. With its strong performance across multiple benchmarks and human evaluations, FancyVideo sets a new precedent for future developments in dynamic and consistent video generation.