New progress in high dynamic video generation
Make Pixels Dance: High-Dynamic Video Generation
New progress in high dynamic video generation
Preface
Dynamic video generation has always been an important and challenging goal in the field of artificial intelligence. In particular, it is even more difficult to generate high-quality videos with complex scenes and rich actions. Many existing video generation models mainly focus on generating videos from text descriptions, and often can only output videos with very small motion amplitudes, which is also a difficulty in the industry.
Recently, ByteDance researchers have proposed a very creative method - PixelDance, which uses prior knowledge of images to guide the video generation process, thus greatly improving the dynamics of the video. Specifically, in addition to using text description, this method also uses the first frame image of the video > andThe last frame image is used as a condition to generate the intermediate dynamic video content.
The first frame of the image mainly provides detailed information about complex scenes and objects.
The last frame of the image guides the video in the desired direction.
In order to improve the generalization of the model, the researchers used some clever data enhancement techniques to avoid the model strictly copying the last frame of the image as the end of the video.
PixelDance has achieved very significant performance improvements on both MSR-VTT and UCF-101 public data sets.
What is particularly impressive is that this method of using prior knowledge of images can even allow the model to generate some domains that do not exist in the training data at all, such as animation, science fiction and other style videos. I believe that this approach of guiding the model to focus on the dynamics of generated content opens up new ideas for dynamic video generation and will also have a profound impact on the synthesis of creative video content. In the next step, further expanding the model scale and using higher-quality open domain video data for training will be useful directions for exploration.
Overall, this research sets a new benchmark for complex and highly dynamic video generation and deserves attention. I look forward to further research in the future, allowing machines to create long videos with coherent plots like movie directors, or even smart movies!
Paper address: https://arxiv.org/abs/2311.10982
Official website address: https://makepixelsdance.github.io
Video generation mode
The first mode is the basic mode. Users only need to provide a guidance image and corresponding text description to generate a highly consistent and dynamic video.
The second mode is the advanced magic mode, which provides users with greater space for imagination and creation. In this mode, users need to submit two guidance images and related text descriptions to generate more challenging video content.
Summary
How to produce high-dynamic videos with rich movements and complex visual effects is a major challenge facing the field of artificial intelligence.
Unfortunately, current state-of-the-art video generation methods, which mainly focus on text-to-video generation, tend to produce video clips with minimal movements, although maintaining high fidelity.
We believe that relying solely ontext instructions is insufficient and sub-optimal for video generation. In this paper, we introduce PixelDance, a new method based on the diffusion model that combines the image instructions of the first and last frames Combined with video-generated text instructions.
Comprehensive experimental results show that pixeldance trained using public data shows significantly better proficiency in synthesizing videos of complex scenes and complex actions, setting a new standard for video generation
Ten questions about the paper
- What problem is the paper trying to solve?
This paper addresses the problem of highly dynamic video generation, including complex scenes and fine motion. Existing text-generated videos produce short videos with small movements.
- Is this a new problem?
It is not a new problem, it is a key problem in the field of video generation. The article proposes a graphic and video generation framework to solve this problem.
- What scientific hypothesis does this article test?
Previous research has used text to generate videos. The author believes that relying solely on text instructions is not enough for video generation, and the first and last frame instructions should be added. This significantly improves the quality and dynamics of video generation.
- What are the relevant studies? How to classify? Who are the noteworthy researchers in the field on this topic?
Related research includes text-to-video generation process based on GAN, Transfomers with VQVAE. Key researchers include Songwei Ge, Jonathan Ho, etc.
- What is the key to the solution mentioned in the paper?
The first frame, text prompt, and last frame are used to guide the video generation process. And set up exquisite training reasoning skills.
- How were the experiments in the paper designed?
Quantitative evaluation is performed on MSR-VTT and UCF-101 data sets. Conduct ablation experiments for different guidance conditions. Generate long videos for quality analysis.
- What is the data set used for quantitative evaluation? Is the code open source?
The public data sets used include MSR-VTT, UCF-101, LAION-400M, etc. The code is not yet open source.
MSR-VTT is a video retrieval dataset where each video has a description.
UCF-101 is an action recognition dataset with 101 action categories.
- Do the experiments and results in the paper well support the scientific hypothesis that needs to be tested?
Quantitative results and qualitative analysis fully verified the scientific hypothesis.
- What contribution does this paper make?
The main contribution is to propose PixelDance, a graphics and video generation framework, which has achieved new results in public data.
1. Generate longer videos.
2. More actions.
3. Natural lens conversion.
- What’s next? Is there any work that can be further developed?
The next step can be to use higher quality video data for training, fine-tune in specific domains, expand the model size, etc.
experiment
data set
MSR-VTT: is a video retrieval data set, each video has a description.
UCF-101: is an action recognition dataset with 101 action categories.
Quantitative evaluation indicators
1.FID and FVD both measure the distribution distance between the generated video and real data.
2.IS evaluates the quality of the generated video.
3. CLIPSIM estimates the similarity between the generated video and the corresponding text.
Based on the MSR-VTT data set, the indicators are CLIPSIM and FVD.
It can be seen that the distribution of the video generated by PixelDance is closest to the real data, and most similar to the text description.
Based on the UCF-101 data set, the indicators are IS, FID and FVD.
It can be seen that the distribution of the video generated by PixelDance is closest to the real data, and the video generated by PixelDance has the highest quality.
ablation study
This paper conducts ablation experiments to evaluate key components in the PixelDance model, including the role of text instructions, first frame image instructions, and last frame image instructions.
- Compared with the baseline T2V model that only uses text instructions, PixelDance achieves significantly better generated video quality, proving the effectiveness of image instructions.
- When text instructions are removed, the FID and FVD indicators of the generated video decrease to a certain extent. This shows that text instructions can help enhance the cross-frame consistency of key elements in the video, such as character clothing, etc.
- When trained without the last frame image instruction, the quality of the video generated by the model also decreased. This demonstrates the utility of the last frame in guiding video generation toward a desired end state.
- Even if the last frame of the image is not provided in the evaluation, the model trained with this instruction still outperforms the model without it. This shows that the last frame image instruction can enhance the model's modeling ability and timing consistency of motion dynamics.
- Overall, the combination of various instructions can allow the model to achieve huge improvements in complex video generation. Each instruction helps the model learn different aspects of how to generate coherent, realistic videos.
Therefore, the ablation experiments in this paper fully verify the value provided by different instructions, and also explain why PixelDance can generate videos with higher quality and richer actions.
Training and inference skills
In order to better utilize image instructions to guide video generation, this paper designs some unique training and inference technologies:
training techniques
(1) The first frame image instruction uses the first frame of the real video, forcing the model to strictly follow this instruction.
(2) The last frame instruction randomly selects one of the last three frames of the real video to increase sample diversity.
(3) Add noise to the last frame instruction to enhance the robustness of the model.
(4) Randomly discard the last frame instruction with a certain probability to prevent the model from overly relying on this instruction.
reasoning techniques
(1) The first τ step uses the last frame instruction to guide the video generation to the desired ending.
(2) The instruction is discarded in the last T-τ step to generate smoother and more coherent content.
(3) The size of τ controls the impact of the last frame instruction.
These techniques allow the model to learn the intrinsic dynamics of video content during the training phase, and also allow the video generated during the inference process to follow instructions without being rigid. Adjustment of τ also provides flexible generation control.
Text + first frame
Text + first frame + last frame
importance of text
The role of τ
Without this parameter, the generated video ends abruptly at the last frame given.
In contrast, adding this parameter will make the generated video smoother and more consistent in time.
More applications
- sketch command
- video editing
Although the PixelDance model of this paper is mainly based on text instructions and image instructions for video generation, the article also explores the use of simple image sketches as the last frame instructions.
- With fine-tuning, the PixelDance model can use sketch images containing simple outlines as last-frame instructions to guide the video generation process.
- Similar to real images, the scene and subject information provided by the last frame of the sketch can effectively guide the model to synthesize realistic video content.
- Although the training data does not contain any sketch videos, PixelDance demonstrates that by learning dynamics and temporal consistency, zero-shot generalization to new image domains (such as sketches) can be achieved during the inference stage.
- This illustrates that image instructions can guide the model to learn the essential dynamic knowledge of Encoding-Generating videos by providing a priori scene and content constraints. This knowledge is applicable to various image domains and is not limited to those seen in the training data.
- In general, the introduction of image/sketch instructions provides users with a powerful control interface. Through simple image editing, the video generation process can be guided, and creative functions such as zero-sample video editing can be realized.
This is an interesting finding, and future video generation or editing systems can consider more active interactions with users, because even simple image instructions provided by non-professionals can produce amazing effects. This also provides new ideas for the mass production of creative video content.
