ART$\boldsymbol{\cdot}$V: Auto-Regressive Text-to-Video Generation with Diffusion Models (2311.18834v1)

Abstract: We present ART$\boldsymbol{\cdot}$V, an efficient framework for auto-regressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-shot, ART$\boldsymbol{\cdot}$V generates a single frame at a time, conditioned on the previous ones. The framework offers three distinct advantages. First, it only learns simple continual motions between adjacent frames, therefore avoiding modeling complex long-range motions that require huge training data. Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications. Third, it can generate arbitrarily long videos conditioned on a variety of prompts such as text, image or their combinations, making it highly versatile and flexible. To combat the common drifting issue in AR models, we propose masked diffusion model which implicitly learns which information can be drawn from reference images rather than network predictions, in order to reduce the risk of generating inconsistent appearances that cause drifting. Moreover, we further enhance generation coherence by conditioning it on the initial frame, which typically contains minimal noise. This is particularly useful for long video generation. When trained for only two weeks on four GPUs, ART$\boldsymbol{\cdot}$V already can generate videos with natural motions, rich details and a high level of aesthetic quality. Besides, it enables various appealing applications, e.g., composing a long video from multiple text prompts.

Auto-Regressive Text-to-Video Generation with Diffusion Models: An Expert Overview

The paper introduces ART$\bigcdot$V, a framework designed for efficient auto-regressive video generation utilizing diffusion models. The novelty of this approach lies in its deviation from traditional methods, opting to generate video frames sequentially rather than producing an entire sequence in one operation. This paradigm allows ART$\bigcdot$V to capitalize on the strengths of pre-trained image diffusion models, ensuring high fidelity while maintaining adaptability to various prompt forms, including text and image combinations.

Methodology and Contributions

ART$\bigcdot$V distinguishes itself from existing frameworks by addressing video frame generation on an individual basis, conditioned recursively on preceding frames. This approach mitigates the complications associated with modeling long-range, intricate video motions, which typically demand extensive datasets for training. The design principles of ART$\bigcdot$V showcase several advantages:

1. Simplified Motion Learning: By learning transitional motions between sequential frames, the model circumvents the necessity for large datasets required to understand complex motion over longer video spans.
2. Adaptation of Pre-Trained Models: Minimal network modifications are introduced to maintain the high-fidelity image generation capabilities of the underlying diffusion models pre-trained for image data.
3. Versatility in Generation Length and Prompt Conditions: The framework supports the generation of videos of arbitrary lengths, adapting to diverse prompts, thereby embodying a versatile tool for video generation tasks.

To counter the notorious drifting problem endemic to auto-regressive models—where incremental errors compound in sequential predictions—ART$\bigcdot$V integrates a masked diffusion model. This model selectively extracts information directly from reference frames rather than relying solely on network predictions. Additionally, to enhance coherence, especially for longer sequences, the generation process is conditioned upon the initial frame, a strategy termed “anchored conditioning.”

Results and Findings

ART$\bigcdot$V shows promising results, producing videos characterized by natural motion, detailed richness, and aesthetic quality after a relatively brief training period of two weeks on four GPUs. Notably, the efficiency of ART$\bigcdot$V is underscored by its ability to generate high-quality outputs while minimizing computational demands, as evidenced by comparable, if not superior, performance metrics compared to extant frameworks.

The paper claims notable improvements over existing methods, as demonstrated in metrics like FVD and IS across well-regarded datasets such as UCF-101 and MSR-VTT. Additionally, the modular nature and lack of extensive temporal modeling components offer scalable training opportunities that could further bolster performance with larger datasets and extended training durations.

Implications and Future Directions

The ART$\bigcdot$V framework suggests a significant shift in how video generation tasks might be addressed using diffusion models. By decoupling frame dependencies and focusing on short-range motion modeling, this paper opens avenues for more resource-efficient video generation systems. Future exploration could expand on incorporating additional multimodal data inputs or refining feedback mechanisms within the generation process to further curb error propagation.

The adaptability of ART$\bigcdot$V to support variable-length outputs and seamless integration with existing image diffusion models presents numerous practical applications, from content creation to animation, thereby broadening the potential utility in digital media production.

Conclusion

This paper encapsulates a sophisticated approach to text-to-video generation that balances computational efficiency with output quality. The ART$\bigcdot$V model signifies a pivotal step in diffusion model applications by proposing innovative solutions to long-standing challenges in video sequence coherence and resource-intensive model training. As the landscape of AI-driven content generation continues to evolve, such innovations will be critical in shaping scalable and versatile media production tools.