Sims4Action

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* The Sims4Action Dataset: a videogame-based dataset for Synthetic→Real domain adaptation for human activity recognition. * Goal : Exploring the concept of constructing training examples for Activities of Daily Living (ADL) recognition by playing life simulation video games. * *Sims4Action* dataset is created with the commercial game THE SIMS 4 by executing actions-of-interest within the game in a "top-down" manner. It features ten hours of video material of eight diverse characters and multiple environments. Ten actions are selected to have a direct correspondence to categories covered in the real-life dataset Toyota Smarthome [2] to enable the research of Synthetic→Real transfer in action recognition. * Two benchmarks :* Gaming→Gaming* (training and evaluation on Sims4Action) and *Gaming→Real* (training on Sims4Action, evaluation on the real Toyota Smarthome data [2]). * **Main challenge: *Gaming→Real* domain adaptation** While ADL recognition on gaming data is interesting from a theoretical perspective, the key challenge arises from transferring knowledge learned from simulated data to real-world applications. *Sims4Action* specifically provides a benchmark for this scenario since it describes a *Gaming→Real* challenge, which evaluates models on real videos derived from the existing Toyota Smarthome dataset . # References [1] [Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games.](http://arxiv.org/abs/2107.05617 ) Alina Roitberg*, David Schneider*, Aulia Djamal, Constantin Seibold, Simon Reiß, Rainer Stiefelhagen, In *International Conference on Intelligent Robots and Systems (IROS)*, 2021 (* denotes equal contribution.) [2] Toyota smarthome: Real-world activities of daily living. Srijan Das, Rui Dai, Michal Koperski, Luca Minciullo, Lorenzo Garattoni, Francois Bremond, Gianpiero Francesca, In *International Conference on Computer Vision (ICCV)*, 2019.