แนวคิดหลัก
HARIVO is a novel single-stage method for generating diverse and high-quality videos from text prompts by leveraging the power of frozen, pre-trained text-to-image diffusion models and incorporating innovative architectural designs and loss functions to ensure temporal consistency.
บทคัดย่อ
HARIVO: Harnessing Text-to-Image Models for Video Generation
This research paper introduces HARIVO, a novel method for text-to-video generation that leverages pre-trained text-to-image (T2I) diffusion models, specifically StableDiffusion.
Research Objective:
The paper aims to address the limitations of existing text-to-video (T2V) generation methods, which often require extensive training data, struggle with maintaining temporal consistency, and lack the ability to easily integrate with personalized T2I models.
Methodology:
HARIVO introduces several key innovations:
- Frozen T2I Model: Instead of training the entire T2V model from scratch, HARIVO keeps the T2I model frozen and only trains temporal layers, simplifying the training process and allowing for seamless integration with existing T2I methods like ControlNet and DreamBooth.
- Mapping Network: A mapping network is introduced to transform the diffusion noise prior into a distribution more suitable for generating videos, addressing the challenge of generating temporally correlated frames from independent and identically distributed (IID) noises.
- Frame-wise Tokens: A frame-wise token generator is employed to capture subtle temporal variations across frames, enhancing the model's ability to generate natural and dynamic videos.
- Novel Loss Functions: HARIVO incorporates novel loss functions to ensure temporal smoothness and consistency:
- Temporal Regularized Self-attention (TRS) loss: Penalizes differences in self-attention maps between consecutive frames, promoting smooth transitions.
- Decoupled Contrastive loss on h-space: Enforces semantic consistency within a video by ensuring that all frames share similar features in the bottleneck layer (h-space) of the U-Net.
- Mitigating Gradient Sampling: A mitigating gradient sampling technique is used during inference to prevent abrupt changes between frames, further enhancing the realism and temporal coherence of the generated videos.
Key Findings:
- HARIVO successfully generates high-quality videos with diverse styles, comparable to those produced by larger T2V models, despite its single-stage training process and reliance on a frozen T2I model.
- The proposed method demonstrates superior temporal consistency compared to existing methods, as evidenced by both quantitative metrics and qualitative assessments.
- HARIVO seamlessly integrates with off-the-shelf personalized T2I models, enabling the generation of personalized videos without requiring additional training.
Main Conclusions:
HARIVO presents a novel and efficient approach to T2V generation that overcomes several limitations of existing methods. By leveraging the power of frozen T2I models and incorporating innovative architectural designs and loss functions, HARIVO achieves high-quality video generation with improved temporal consistency and flexibility.
Significance:
This research significantly contributes to the field of T2V generation by proposing a more efficient and versatile method that simplifies the training process and expands the creative possibilities for video synthesis.
Limitations and Future Research:
The paper acknowledges that HARIVO's reliance on StableDiffusion inherits its limitations, such as difficulties in accurately generating human hands and limbs. Future research could explore integrating HARIVO with other T2I models or techniques that address these limitations. Additionally, the ethical implications of increasingly realistic video generation technology warrant further investigation.
สถิติ
FVD score of 787.87 on UCF101 dataset.
CLIP Similarity score of 0.2948 on MSR-VTT dataset.
User study with 50 participants and over 5K votes showed higher ratings for HARIVO in motion, consistency, and overall quality compared to VideoLDM, PYoCo, and ModelScope.
คำพูด
"We present a method to create diffusion-based video models from pretrained Text-to-Image (T2I) models."
"Our method, built on the frozen StableDiffusion model, simplifies training processes and allows for seamless integration with off-the-shelf models like ControlNet and DreamBooth."
"Our model shows temporally consistent video generation although it is trained on a public dataset (WebVid-10M), whereas many existing works are trained on in-house datasets."