TikTok Dataset (Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos)

Introduced by Jafarian et al. in Self-supervised 3D Representation Learning of Dressed Humans from Social Media Videos

We learn high fidelity human depths by leveraging a collection of social media dance videos scraped from the TikTok mobile social networking application. It is by far one of the most popular video sharing applications across generations, which include short videos (10-15 seconds) of diverse dance challenges as shown above. We manually find more than 300 dance videos that capture a single person performing dance moves from TikTok dance challenge compilations for each month, variety, type of dances, which are moderate movements that do not generate excessive motion blur. For each video, we extract RGB images at 30 frame per second, resulting in more than 100K images. We segmented these images using Removebg application, and computed the UV coordinates from DensePose.

Download TikTok Dataset:

  • Please use the dataset only for the research purpose.

  • The dataset can be viewed and downloaded from the Kaggle page. (you need to make an account in Kaggle to be able to download the data. It is free!)

  • The dataset can also be downloaded from here (42 GB). The dataset resolution is: (1080 x 604)

  • The original YouTube videos corresponding to each sequence and the dance name can be downloaded from here (2.6 GB).


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