Perceiving human body motion is critical for multimodal systems that understand and assist people. Yet current evaluations of Multimodal Large Language Models (MLLMs) largely focus on objects, scenes,
SOMA is a benchmark assessing fine-grained egocentric body-motion reasoning in multimodal models, revealing substantial gaps versus human performance and providing training data and a multimodal baseline to advance the field.
Perceiving human body motion is critical for multimodal systems that understand and assist people. Yet current evaluations of Multimodal Large Language Models (MLLMs) largely focus on objects, scenes, and coarse actions, leaving their ability to reason about the body itself poorly assessed. This is especially challenging from the egocentric perspective, in which the body is only partially visible and motion must be inferred from occluded and temporally distributed cues. We introduce SOMA, a benchmark for fine-grained body-motion understanding from egocentric video, comprising 1,632 human-verified questions across three capability axes: body configuration, motion dynamics, and temporal…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
SOMA: A Benchmark for Fine-Grained Egocentric Behavior and Body-Motion Understanding was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 8 checks on 2 different days. Its best position was #21 of 30, reached on Nov 3, 15:28 UTC. It dropped off the list after Nov 4, 01:28 UTC.
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Not at the moment. It was last on the list on Nov 4, 01:28 UTC, after 2 days of trending.
#21 on Hugging Face · Daily papers, on Nov 3, 15:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 2 days (8 checks) since.