What is EWMBench, what exactly does it measure (scene, motion, semantics), what inputs does it need, and how do I run it?
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.
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.
Curator qualifications: All 8 excerpts located; sample-count and EWMBM/EWMBench naming conflicts kept.
Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.
Structured fields
{
"citations": [
{
"url": "https://arxiv.org/html/2505.09694v2",
"note": "candidate evidence excerpts"
},
{
"url": "https://github.com/AgibotTech/EWMBench/tree/3a5531c0e6b6be51431251ed5617b9e48c852098",
"note": "candidate evidence excerpts"
}
]
}Evidence claims
- [sourced_fact] 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 source
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | permitted | permitted | unknown | unknown | Applies to this original summary only, not to the cited sources. |