{"schema":"record/v1","id":"art_01M5MV9K2RAM7RC7WE57TMMHXT","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Where is AI documented in 360-film attention work (saliency prediction, script-driven object highlighting, generated 360 scenes), and what evidence supports it?","summary":"Two documented AI connections were found. (1) Sitzmann et al. adapt existing 2D saliency predictors to 360 panoramas by multiplying with a learned equator bias and propose saliency-based cut alignment (static-panorama data, not reproduced). (2) da Silva et al. (SVR 2024, arXiv 2026, CC BY 4.0) chain Grounding DINO and Segment Anything to find the object named in a text script and apply a vignette to it in a 360 tour; their text shows frames from a university video tour, one SAM failure, and a conclusion that 'user experience' improves, but the extract shows no user study or measurement supporting that claim. No source linking generated 360 scenes to viewer-attention outcomes was found in a bounded search.","status":"active","body":{"text":"Two documented AI connections were found. (1) Sitzmann et al. adapt existing 2D saliency predictors to 360 panoramas by multiplying with a learned equator bias and propose saliency-based cut alignment (static-panorama data, not reproduced). (2) da Silva et al. (SVR 2024, arXiv 2026, CC BY 4.0) chain Grounding DINO and Segment Anything to find the object named in a text script and apply a vignette to it in a 360 tour; their text shows frames from a university video tour, one SAM failure, and a conclusion that 'user experience' improves, but the extract shows no user study or measurement supporting that claim. No source linking generated 360 scenes to viewer-attention outcomes was found in a bounded search.\n\nCurator qualifications: Documented AI connections: Sitzmann saliency adaptation with equator bias; da Silva et al. Grounding DINO + SAM pipeline (CC BY 4.0 paper per producer).\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/1612.04335v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2603.16875v1","note":"candidate evidence excerpts"}]},"categories":["T11","T15","T19"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/html/1612.04335v2","claim":"Two documented AI connections were found. (1) Sitzmann et al. adapt existing 2D saliency predictors to 360 panoramas by multiplying with a learned equator bias and propose saliency-based cut alignment (static-panorama data, not reproduced). (2) da Silva et al. (SVR 2024, arXiv 2026, CC BY 4.0) chain Grounding DINO and Segment Anything to find the object named in a text script and apply a vignette to it in a 360 tour; their text shows frames from a university video tour, one SAM failure, and a conclusion that 'user experience' improves, but the extract shows no user study or measurement supporting that claim. 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(1) Sitzmann et al. adapt existing 2D saliency predictors to 360 panoramas by multiplying with a learned equator bias and propose saliency-based cut alignment (static-panorama data, not reproduced). (2) da Silva et al. (SVR 2024, arXiv 2026, CC BY 4.0) chain Grounding DINO and Segment Anything to find the object named in a text script and apply a vignette to it in a 360 tour; their text shows frames from a university video tour, one SAM failure, and a conclusion that 'user experience' improves, but the extract shows no user study or measurement supporting that claim. No source linking generated 360 scenes to viewer-attention outcomes was found in a bounded search.\n\nCurator qualifications: Documented AI connections: Sitzmann saliency adaptation with equator bias; da Silva et al. Grounding DINO + SAM pipeline (CC BY 4.0 paper per producer).\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/1612.04335v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2603.16875v1","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Where is AI documented in 360-film attention work (saliency prediction, script-driven object highlighting, generated 360 scenes), and what evidence supports it?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Two documented AI connections were found. (1) Sitzmann et al. adapt existing 2D saliency predictors to 360 panoramas by multiplying with a learned equator bias and propose saliency-based cut alignment (static-panorama data, not reproduced). (2) da Silva et al. (SVR 2024, arXiv 2026, CC BY 4.0) chain Grounding DINO and Segment Anything to find the object named in a text script and apply a vignette to it in a 360 tour; their text shows frames from a university video tour, one SAM failure, and a conclusion that 'user experience' improves, but the extract shows no user study or measurement supporting that claim. No source linking generated 360 scenes to viewer-attention outcomes was found in a bounded search.","evidence":{"claims":[{"url":"https://arxiv.org/html/1612.04335v2","claim":"Two documented AI connections were found. (1) Sitzmann et al. adapt existing 2D saliency predictors to 360 panoramas by multiplying with a learned equator bias and propose saliency-based cut alignment (static-panorama data, not reproduced). 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