{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T01:48:52Z","timestamp":1783129732752,"version":"3.54.6"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>\n            We present the first competitive drawing agent\n            <jats:italic>Pixelor<\/jats:italic>\n            that exhibits humanlevel performance at a Pictionary-like sketching game, where the participant whose sketch is recognized first is a winner. Our AI agent can autonomously sketch a given visual concept, and achieve a recognizable rendition as quickly or faster than a human competitor. The key to victory for the agent's goal is to learn the optimal stroke sequencing strategies that generate the most recognizable and distinguishable strokes first. Training\n            <jats:italic>Pixelor<\/jats:italic>\n            is done in two steps. First, we infer the stroke order that maximizes early recognizability of human training sketches. Second, this order is used to supervise the training of a sequence-to-sequence stroke generator. Our key technical contributions are a tractable search of the exponential space of orderings using neural sorting; and an improved Seq2Seq Wasserstein (S2S-WAE) generator that uses an optimal-transport loss to accommodate the multi-modal nature of the optimal stroke distribution. Our analysis shows that\n            <jats:italic>Pixelor<\/jats:italic>\n            is better than the human players of the\n            <jats:italic>Quick, Draw!<\/jats:italic>\n            game, under both AI and human judging of early recognition. To analyze the impact of human competitors' strategies, we conducted a further human study with participants being given unlimited thinking time and training in early recognizability by feedback from an AI judge. The study shows that humans do gradually improve their strategies with training, but overall\n            <jats:italic>Pixelor<\/jats:italic>\n            still matches human performance. The code and the dataset are available at http:\/\/sketchx.ai\/pixelor.\n          <\/jats:p>","DOI":"10.1145\/3414685.3417840","type":"journal-article","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T21:51:05Z","timestamp":1606513865000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["Pixelor"],"prefix":"10.1145","volume":"39","author":[{"given":"Ayan Kumar","family":"Bhunia","sequence":"first","affiliation":[{"name":"University of Surrey, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayan","family":"Das","sequence":"additional","affiliation":[{"name":"University of Surrey, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umar Riaz","family":"Muhammad","sequence":"additional","affiliation":[{"name":"University of Surrey, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongxin","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Surrey, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy M.","family":"Hospedales","sequence":"additional","affiliation":[{"name":"University of Edinburgh, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Xiang","sequence":"additional","affiliation":[{"name":"University of Surrey, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulia","family":"Gryaditskaya","sequence":"additional","affiliation":[{"name":"University of Surrey, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Zhe","family":"Song","sequence":"additional","affiliation":[{"name":"University of Surrey, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432","author":"Bengio Yoshua","year":"2013","unstructured":"Yoshua Bengio , Nicholas L\u00e9onard , and Aaron Courville . 2013. 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In International Conference on Learning Representations."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3414685.3417840","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3414685.3417840","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:03:15Z","timestamp":1750197795000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3414685.3417840"}},"subtitle":["a competitive sketching AI agent. so you think you can sketch?"],"short-title":[],"issued":{"date-parts":[[2020,11,27]]},"references-count":57,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,12,31]]}},"alternative-id":["10.1145\/3414685.3417840"],"URL":"https:\/\/doi.org\/10.1145\/3414685.3417840","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,27]]},"assertion":[{"value":"2020-11-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}