Motion Captioning
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Benchmarks
HumanML3D
KIT Motion-Language
Most implemented
MotionGPT: Human Motion as a Foreign Language
Transformer with Controlled Attention for Synchronous Motion Captioning
Motion2Language, unsupervised learning of synchronized semantic motion segmentation
Guided Attention for Interpretable Motion Captioning
Papers
Identity-Aware Human-Object Interaction Motion Captioning
Existing human-object interaction (HOI) motion captioning methods typically describe what happens while referring to the subject using generic terms such as "a person" or "someone", without grounding the caption in subje…
Motion CaptioningMotionAtlas: Detailed Region Captioning for Motion-Centric Videos
We propose MotionAtlas, a system for detailed captioning of motion-centric videos, comprising (1) a dedicated human-annotated benchmark, (2) a scalable, high-quality pipeline to construct training samples, and (3) a fami…
Motion CaptioningFine-grained Human Motion Understanding with Language Models
In this work, we propose \methodname, an LLM-based model for fine-grained human motion understanding that represents motion as a sequence of skeletal poses with explicit timestamps for each pose. Each pose encodes body j…
Question AnsweringMotion CaptioningAnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild
As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild. But inertial signals are highly dependent on the sensing setup, includ…
Cross-Modal RetrievalActivity RecognitionMotion CaptioningEncoder-Free Human Motion Understanding via Structured Motion Descriptions
The world knowledge and reasoning capabilities of text-based large language models (LLMs) are advancing rapidly, yet current approaches to human motion understanding, including motion question answering and captioning, h…
Question AnsweringMotion CaptioningUniversal Skeleton Understanding via Differentiable Rendering and MLLMs
Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet cannot process structured, non-visual data such as human skeletons. Existing methods either compress skeleton dynamics into lossy fea…
Action RecognitionQuestion AnsweringMotion Captioning