Developing Animation System Techniques
Meta, the tech giant known for its advancements in AI and virtual reality, has made significant strides in the realm of animating human-like figures and understanding human motion in video. However, a specific dataset tailored for animating amateur drawings of human-like figures remains elusive, according to available information.
Meta's V-JEPA 2 (Video Joint Embedding Predictive Architecture 2) is a large-scale video-trained world model that excels in understanding, predicting human actions, and enabling zero-shot planning and robot control in new environments. Despite its impressive capabilities, details about datasets for amateur drawings animation are not explicitly provided.
A related Meta effort, the Meta Motivo model, controls physics-based humanoid avatars to accomplish whole-body tasks, suggesting Meta's focus on controlling animated humanoid figures in simulated environments. However, there is no direct mention of a dataset for amateur drawings animation.
Other publicly available datasets for human motion, such as Rigplay's large-scale motion capture data, provide detailed human motion data but are not Meta datasets and focus on 3D motion capture rather than amateur drawing animations.
The DeepAction dataset, which includes video clips generated by AI text-to-video models and matched real videos depicting diverse human actions, is helpful for training AI on human motion. Yet, it is not targeted specifically at animating amateur drawings.
Recently, Meta has created an annotated dataset featuring nearly 180,000 amateur drawings of human-like figures on paper. The dataset, unique in its focus on amateur drawings, includes annotations indicating the location of the figure, which pixels belong to the figure, and the location of the figure's joints. However, the dataset does not include any information about the age, gender, or ethnicity of the figures in the drawings.
Despite its potential, the dataset is not available for public download. It appears to be used by Meta to train AI systems to animate artwork, specifically amateur drawings, not professional artwork or other types of images. The dataset is also created by Meta, not another organization or individual.
For those interested in accessing datasets related to AI animation of human figures, Meta AI's official announcement channels or research repository pages could be a starting point for the latest dataset releases. The DeepAction dataset and commercially available motion capture datasets like Rigplay are also worth exploring.
As Meta continues to push the boundaries of AI and animation, updates on new datasets regarding the animation of amateur drawings may emerge. Keeping an eye on Meta AI's official websites, research publications, or platforms like FAIR’s GitHub repositories, where datasets and code are often publicly shared, might provide insights into future developments. Additionally, contacting Meta AI directly or following their AI research blog could provide updates on new datasets in this field.
Meta's recent creation, an annotated dataset of nearly 180,000 amateur drawings of human-like figures, is unique in its focus and could be useful for training AI to animate such drawings. However, it doesn't provide information about the age, gender, or ethnicity of the figures.
Although Meta AI has not publicly released a dataset specifically tailored for animating amateur drawings, it could be worth following their official announcement channels or research repository pages for any future updates in this field, as they continue to advance in AI and animation technology.
