{"schema":"record/v1","id":"art_01M5MC5MRPSTTB4RFTYGQZXQG7","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What is EWMBench, what exactly does it measure (scene, motion, semantics), what inputs does it need, and how do I run it?","summary":"EWMBench (AgiBot, arXiv v2) scores image-text-to-video models on robot manipulation: given an initial scene image, an instruction and optionally a 6D trajectory, the model generates frames that are compared with ground-truth AgiBot-World episodes. It measures scene consistency (fine-tuned DINOv2 patch similarity), end-effector motion (Hausdorff, normalised DTW and a velocity/acceleration Wasserstein term from a fine-tuned YOLO-World detector), and semantics (a Qwen2.5-VL caption compared by BLEU and CLIP score, a diversity term, and a logic check). It is robot-specific, fixed-viewpoint and needs a ground-truth trajectory per case.","status":"active","body":{"text":"EWMBench (AgiBot, arXiv v2) scores image-text-to-video models on robot manipulation: given an initial scene image, an instruction and optionally a 6D trajectory, the model generates frames that are compared with ground-truth AgiBot-World episodes. It measures scene consistency (fine-tuned DINOv2 patch similarity), end-effector motion (Hausdorff, normalised DTW and a velocity/acceleration Wasserstein term from a fine-tuned YOLO-World detector), and semantics (a Qwen2.5-VL caption compared by BLEU and CLIP score, a diversity term, and a logic check). It is robot-specific, fixed-viewpoint and needs a ground-truth trajectory per case.\n\nCurator qualifications: All 8 excerpts located; sample-count and EWMBM/EWMBench naming conflicts kept.\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/2505.09694v2","note":"candidate evidence excerpts"},{"url":"https://github.com/AgibotTech/EWMBench/tree/3a5531c0e6b6be51431251ed5617b9e48c852098","note":"candidate evidence excerpts"}]},"categories":["T06","T15","T20"],"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/2505.09694v2","claim":"EWMBench (AgiBot, arXiv v2) scores image-text-to-video models on robot manipulation: given an initial scene image, an instruction and optionally a 6D trajectory, the model generates frames that are compared with ground-truth AgiBot-World episodes. It measures scene consistency (fine-tuned DINOv2 patch similarity), end-effector motion (Hausdorff, normalised DTW and a velocity/acceleration Wasserstein term from a fine-tuned YOLO-World detector), and semantics (a Qwen2.5-VL caption compared by BLEU and CLIP score, a diversity term, and a logic check). 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It measures scene consistency (fine-tuned DINOv2 patch similarity), end-effector motion (Hausdorff, normalised DTW and a velocity/acceleration Wasserstein term from a fine-tuned YOLO-World detector), and semantics (a Qwen2.5-VL caption compared by BLEU and CLIP score, a diversity term, and a logic check). It is robot-specific, fixed-viewpoint and needs a ground-truth trajectory per case.\n\nCurator qualifications: All 8 excerpts located; sample-count and EWMBM/EWMBench naming conflicts kept.\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/2505.09694v2","note":"candidate evidence excerpts"},{"url":"https://github.com/AgibotTech/EWMBench/tree/3a5531c0e6b6be51431251ed5617b9e48c852098","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"What is EWMBench, what exactly does it measure (scene, motion, semantics), what inputs does it need, and how do I run 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":"EWMBench (AgiBot, arXiv v2) scores image-text-to-video models on robot manipulation: given an initial scene image, an instruction and optionally a 6D trajectory, the model generates frames that are compared with ground-truth AgiBot-World episodes. It measures scene consistency (fine-tuned DINOv2 patch similarity), end-effector motion (Hausdorff, normalised DTW and a velocity/acceleration Wasserstein term from a fine-tuned YOLO-World detector), and semantics (a Qwen2.5-VL caption compared by BLEU and CLIP score, a diversity term, and a logic check). It is robot-specific, fixed-viewpoint and needs a ground-truth trajectory per case.","evidence":{"claims":[{"url":"https://arxiv.org/html/2505.09694v2","claim":"EWMBench (AgiBot, arXiv v2) scores image-text-to-video models on robot manipulation: given an initial scene image, an instruction and optionally a 6D trajectory, the model generates frames that are compared with ground-truth AgiBot-World episodes. It measures scene consistency (fine-tuned DINOv2 patch similarity), end-effector motion (Hausdorff, normalised DTW and a velocity/acceleration Wasserstein term from a fine-tuned YOLO-World detector), and semantics (a Qwen2.5-VL caption compared by BLEU and CLIP score, a diversity term, and a logic check). It is robot-specific, fixed-viewpoint and needs a ground-truth trajectory per case.","locator":"candidate evidence excerpts","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"source_inspected","execution_record_ids":[]},"language":"en","categories":["T06","T15","T20"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":["CAT:T06.AIC.13","CAT:WM.08"],"confidence":"Not stated; see limitations.","limitations":["Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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