Game of Sketches: Deep Recurrent Models of Pictionary-Style Word Guessing


  • Ravi Kiran Sarvadevabhatla Indian Institute of Science
  • Shiv Surya Indian Institute of Science
  • Trisha Mittal Indian Institute of Science
  • R. Venkatesh Babu Indian Institute of Science


Pictionary, Sketches, AI, Deep Learning


The ability of machine-based agents to play games in human-like fashion is considered a benchmark of progress in AI. In this paper, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, an elementary version of Visual Question Answering task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data. Notably, Sketch-QA involves asking a fixed question ("What object is being drawn?") and gathering open-ended guess-words from human guessers. To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor. We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. We also conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.




How to Cite

Sarvadevabhatla, R. K., Surya, S., Mittal, T., & Babu, R. V. (2018). Game of Sketches: Deep Recurrent Models of Pictionary-Style Word Guessing. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from