Model Card for FollowYourPose V1
Model Details
Model Description
The authors note in the
assoicated paper:
Pose-Guided Text-to-Video Generation.
We propose an efficient training scheme to empower the ability of the pretrained text-to-image model (i.e., Stable Diffusion) to generate pose-controllable character videos with minimal data requirements.
We can generate various high-definition pose-controllable character videos that are well-aligned with the pose sequences and the semantics of text prompts.
- Developed by: Yue Ma, Yingqing He, Xintao Wang, Ying Shan, Xiu Li, Qifeng Chen
- Shared by: Yue (Jack) Ma
- Model type: Text-to-Video
- Language(s) (NLP): [More Information Needed]
- License: MIT
- Finetuned from model: Stable Diffusion T2I
Model Sources
Uses
Direct Use
Text to Video
Downstream Use
- Character replacement
- Background change
- Style transfer
- Multiple characters
Out-of-Scope Use
Note: This section as adpated from the Stable Diffusion v1 model card.
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
Bias, Risks, and Limitations
Note: This section is adpted from the DALLE-MINI model card.
The model should not be used to intentionally create or disseminate videos that create hostile or alienating environments for people. This includes generating videos that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
1from diffusers import DiffusionPipeline
2
3pipeline = DiffusionPipeline.from_pretrained("YueMafighting/FollowYourPose_v1")
Training Details
Training Data
Stage 1 Training Data: Pose-Controllable Text-to-Image Gen- eration.
The authors note in the
assoicated paper that they colected
the human skeleton images in the LAION by Mpose, only retaining images that could be detected more than 50% of the key point
which formed a dataset named LAION-Pose from.
The autheors note in the
associated paper about LAION-Pose:
This dataset contains diverse human-like characters with various back- ground contexts.
Stage 2 Training Data: Video Generation via Pose-free Videos.
However, the stage 1 model can generate similar pose videos yet the background is inconsistent.
Thus, we further finetune the model from our first stage on the pose- free video dataset HDVLIA
This dataset contains con- tinuous in-the-wild video text pairs.
Training Procedure
Preprocessing
The authors note in the
assoicated paper:
We learn a zero-initialized convolutional encoder to encode the pose information
We finetune the motion of the above network via a pose-free video dataset by adding the learnable temporal self-attention and reformed cross-frame self-attention blocks.
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
The authors note in the
assoicated paper:
- CLIP score (CS): CLIP score or video-text alignment. We compute CLIP score for each frame and then average them across all frames.
The final CLIP score is calculated on 1024 video samples.
- Quality (QU): We conduct the human evaluation of videos’ quality across a test set containing 32 videos.
In detail, we display three videos in random order and request the evaluators to iden- tify the one with superior quality
- Pose Accuracy (FQ): We regard the input pose sentence as ground truth video and evaluate the average precision of the skeleton on 1024 video samples.
For a fair comparison, we adopt the same pose detector for both the processing of LAION-Pose collecting and evaluation.
- Frame Consistency (FC): Following we report frame consistency measured via CLIP cosine similarity of consecutive frames
Results
| Method | CS | QU (%) | PA (%) | FC (%) |
|---|
| Tune-A-Video | 23.57 | 34.81 | 27.74 | 93.78 |
| ControlNet | 22.31 | 6.69 | 33.23 | 54.35 |
| T2I adapter | 22.42 | 8.27 | 33.47 | 53.86 |
| FollowYourPose | 24.09 | 50.23 | 34.92 | 93.36 |
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: 8 NVIDIA Tesla 40G-A100 GPUs
- Hours used: 48
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
The authors note in the
assoicated paper:
To make the pre-trained T2I model suitable for video inputs, we make several key modifications.
Firstly, we add extra temporal self-attention layers, to the stable diffusion network.
Secondly, inspired by the recent one-shot video generation model, we reshape the attention to cross-frame attention for better content consistency.
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
1@article{ma2023follow,
2 title={Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos},
3 author={Ma, Yue and He, Yingqing and Cun, Xiaodong and Wang, Xintao and Shan, Ying and Li, Xiu and Chen, Qifeng},
4 journal={arXiv preprint arXiv:2304.01186},
5 year={2023}
6}
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
Yue Ma in collaberation with
Ezi Ozoani and the Hugging Face team.
Model Card Contact
If you have any questions or ideas to discuss, feel free to contact
Yue Ma or
Yingqing He or
Xiaodong Cun.
