Seeing is Moving: Predicting Hand Actions from Video and Language

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Author: Denis Avetisyan

Researchers have developed a new framework that accurately forecasts 3D hand movements by combining visual understanding with linguistic context from human interaction videos.

The EgoMAN model establishes a modular reasoning-to-motion framework wherein a QwenVL-7B-based Reasoning Module extracts semantic and spatial features to generate trajectory tokens-incorporating both waypoints and intent-which then serve as conditional input for a Flow Matching-based Motion Expert, effectively bridging semantic understanding to precise 6DoF trajectory prediction from egocentric visual input and past motion.
The EgoMAN model establishes a modular reasoning-to-motion framework wherein a QwenVL-7B-based Reasoning Module extracts semantic and spatial features to generate trajectory tokens-incorporating both waypoints and intent-which then serve as conditional input for a Flow Matching-based Motion Expert, effectively bridging semantic understanding to precise 6DoF trajectory prediction from egocentric visual input and past motion.

This work introduces EgoMAN, a large-scale dataset and a reasoning-to-motion approach utilizing flow matching for long-horizon 6DoF hand trajectory prediction with state-of-the-art performance.

Predicting human hand movements remains challenging due to a scarcity of datasets linking action understanding with detailed motion data. This paper, ‘Flowing from Reasoning to Motion: Learning 3D Hand Trajectory Prediction from Egocentric Human Interaction Videos’, addresses this gap by introducing EgoMAN, a large-scale dataset and a novel reasoning-to-motion framework that predicts 3D hand trajectories grounded in visual, linguistic, and interaction context. By aligning semantic reasoning with dynamic motion generation via trajectory tokens, our approach achieves state-of-the-art accuracy and generalization across diverse scenes. Could this integration of reasoning and motion pave the way for more intuitive and responsive robotic manipulation?

The Imperative of Accurate Trajectory Prediction

The ability to accurately forecast six-degree-of-freedom (6DoF) hand trajectories is becoming increasingly vital as humans and robots collaborate more closely, and as immersive virtual reality experiences demand greater realism. Precise prediction isn’t merely about anticipating where a hand will be, but also its orientation and how it will interact with objects in the environment. For robotic systems designed to assist with tasks – from surgery to assembly – anticipating a human’s hand movements allows for proactive, safe, and intuitive collaboration. Similarly, in virtual or augmented reality, correctly predicting hand motions is fundamental to creating believable interactions, reducing latency, and enabling realistic manipulation of virtual objects – ultimately enhancing the sense of presence and immersion for the user.

Current methodologies for anticipating human movement, including those centered on identifying environmental affordances – the possibilities for action an environment offers – and systems combining visual data with language and action understanding, frequently falter when confronted with intricate scenarios. These approaches often struggle to move beyond simple predictions, proving inadequate when humans engage in subtle gestures, manipulate multiple objects simultaneously, or respond to rapidly changing circumstances. The limitations stem from an inability to effectively interpret the underlying intent driving the action; a system might recognize a hand approaching a doorknob, but struggle to predict how that hand will grasp it – a firm grip for opening, a gentle touch for knocking, or a hesitant reach indicating uncertainty. Consequently, predictions become brittle and unreliable, hindering the development of truly adaptive and intuitive human-robot interactions or immersive virtual reality experiences.

Current approaches to predicting human motion often falter when faced with the complexities of real-world interactions because they struggle to bridge the gap between understanding what a person intends and planning the dynamic movements to achieve it. While systems can identify objects and even infer high-level actions, they frequently lack the capacity to seamlessly translate this semantic awareness into detailed, physically plausible trajectories. This disconnect limits their ability to anticipate subtle changes in intent, adapt to unforeseen obstacles, or generalize to novel scenarios-essential qualities for robust performance in applications like collaborative robotics or immersive virtual environments. Consequently, predictions can appear rigid or unnatural, hindering effective human-robot interaction and diminishing the sense of presence in virtual reality.

EgoMAN accurately generates six-dimensional hand trajectories that align with both intended actions and the surrounding environment across a variety of activities.
EgoMAN accurately generates six-dimensional hand trajectories that align with both intended actions and the surrounding environment across a variety of activities.

A Modular Framework for Reasoning and Motion

EgoMAN is structured as a modular system consisting of two primary components: a Reasoning Module and a Motion Expert. This architecture is designed to facilitate the prediction of six-degree-of-freedom (6DoF) hand trajectories. The modularity allows for independent development and potential replacement of either component without affecting the overall system functionality. The Reasoning Module processes input data to understand the task and environment, while the Motion Expert translates this understanding into executable hand movements, ultimately generating the predicted $6DoF$ trajectory data representing position and orientation over time.

The Reasoning Module within EgoMAN utilizes the Qwen2.5-VL vision-language model to interpret visual input and contextual information. This model processes images and associated data to identify relevant cues pertaining to object semantics, spatial relationships between objects and the hand, and anticipated motion patterns. Specifically, Qwen2.5-VL performs analysis to understand what objects are present, where they are located relative to the hand, and how the hand is likely to interact with them, enabling the system to move beyond simple trajectory prediction and towards understanding the intent behind the motion.

Trajectory Tokens are a discrete sequence generated by the Reasoning Module that encode the anticipated stages of an interactive task. These tokens are not simply classifications of action, but structured data representing specific interaction phases – for example, ‘approach object’, ‘grasp handle’, ‘lift object’, and ‘place on surface’. The token sequence serves as an intermediate representation, translating the semantic understanding of the scene and the desired action into a format directly consumable by the Motion Expert. This allows the system to decompose complex tasks into manageable steps and facilitates the generation of appropriate 6DoF hand trajectories, effectively bridging the gap between high-level reasoning and low-level motor control.

EgoMAN generates diverse 6DoF trajectories from a single image and past motion, demonstrating controllable intent-based motion planning.
EgoMAN generates diverse 6DoF trajectories from a single image and past motion, demonstrating controllable intent-based motion planning.

Generating Plausible Trajectories Through Dynamic Modeling

Flow Matching is utilized to train the Motion Expert by learning a conditional velocity field that maps states to immediate future states. This is achieved through a probabilistic diffusion process, where noise is progressively added to the desired velocity field and the network is trained to denoise and recover the original velocity. The resulting learned velocity field is then used during trajectory generation to iteratively predict future states, ensuring dynamically feasible motions by adhering to the learned dynamics. This approach contrasts with traditional methods by directly learning a mapping from states to velocities, rather than relying on hand-engineered cost functions or optimization procedures, and generates smooth trajectories by continuously following the learned velocity field $v(x)$.

Intent Conditioning within the trajectory generation framework utilizes high-level goals and contextual data as inputs to modify the learned velocity field. This is achieved by embedding the desired intent – specified through parameters like target location, object manipulation, or behavioral preference – into the Flow Matching process. By conditioning the velocity field on this intent embedding, the system can dynamically adjust generated trajectories to align with the specified goals and react appropriately to the surrounding environment. The conditioning process allows for the creation of diverse and contextually relevant motions, moving beyond pre-defined behaviors to enable adaptable and purposeful movement planning.

Waypoint Prediction within the trajectory generation framework utilizes a predictive model to estimate future key points along a desired path. This prediction isn’t merely positional; it incorporates velocity and acceleration estimates, providing a dynamically feasible target for trajectory optimization. The system then generates trajectories that minimize deviation from these predicted waypoints while adhering to dynamic constraints, ensuring accurate and purposeful movements. The predicted waypoints act as guiding signals, influencing the trajectory’s shape and direction, and are continuously updated based on incoming sensor data and the evolving environment, allowing for reactive and adaptive path planning.

EgoMAN consistently generates smoother and more accurate 3D hand trajectory and waypoint predictions on the EgoMAN-Bench dataset compared to existing baseline methods.
EgoMAN consistently generates smoother and more accurate 3D hand trajectory and waypoint predictions on the EgoMAN-Bench dataset compared to existing baseline methods.

The EgoMAN Dataset and the Advancement of Predictive Accuracy

The EgoMAN dataset represents a substantial advance in the field of robotic manipulation, providing a large-scale resource specifically designed for predicting 6 Degrees of Freedom (6DoF) hand trajectories. This dataset distinguishes itself through detailed stage-aware annotations, which capture the nuances of human actions as they unfold – from initial approach to final contact. Beyond trajectory data, EgoMAN incorporates question-answer pairs, enabling models to not only predict what a hand will do, but also to understand why. This combination of detailed motion capture and semantic understanding positions EgoMAN as a critical tool for developing more robust and intelligent robotic systems capable of interacting with the world in a human-like manner, and facilitating advancements in areas like assistive robotics and automated task completion.

The efficacy of predicting complex hand movements hinges on recognizing the distinct phases within an action; therefore, a novel training approach leveraging stage-aware supervision was implemented. This technique doesn’t merely predict the trajectory of the hand, but also explicitly teaches the model to identify when specific actions occur within a broader task. By incorporating annotations detailing these interaction stages – such as reaching, grasping, or manipulating – the model gains a contextual understanding crucial for accurate prediction. This granular level of supervision results in a significant performance boost, enabling the system to anticipate future movements with greater precision and to better align predicted trajectories with the semantic meaning of the action being performed, ultimately moving beyond simple path replication towards genuine understanding of human manipulation.

Rigorous evaluation of the EgoMAN dataset reveals state-of-the-art performance in both hand trajectory prediction and semantic understanding of actions. Quantitative results, measured on the challenging EgoMAN-Unseen dataset, demonstrate a low Average Displacement Error (ADE) of $0.140$ meters and a Final Displacement Error (FDE) of $0.125$ meters, indicating precise prediction of hand movements. Further analysis using Dynamic Time Warping (DTW) yielded a score of $0.137$ m, confirming the temporal accuracy of predicted trajectories. Beyond geometric accuracy, the dataset also facilitates strong semantic alignment with actions, as evidenced by a Recall@3 score of $0.11$, signifying the model’s ability to correctly associate predicted movements with their corresponding actions.

EgoMAN demonstrates superior data efficiency compared to EgoMAN-ACT and a static baseline, maintaining strong performance with limited data thanks to its waypoint-based reasoning module and pretraining.
EgoMAN demonstrates superior data efficiency compared to EgoMAN-ACT and a static baseline, maintaining strong performance with limited data thanks to its waypoint-based reasoning module and pretraining.

The pursuit of accurate 3D hand trajectory prediction, as detailed in this work, demands a commitment to provable correctness rather than mere empirical success. EgoMAN’s framework, leveraging vision, language, and motion data, exemplifies this principle. As Andrew Ng states, “AI is not about replacing humans; it’s about augmenting human capabilities.” The ability to accurately predict hand movements isn’t simply about creating a functioning system; it’s about building a reliable foundation for human-robot interaction and assistive technologies, demanding rigorous validation and a mathematically sound approach to trajectory modeling. The stage-aware supervision technique underscores this need for verifiable results, ensuring the predicted motions are not only plausible but demonstrably correct within a given context.

Beyond Mimicry: The Path Forward

The presented work, while demonstrating a laudable convergence of modalities, merely skirts the edges of genuine understanding. Accurate prediction of 3D hand trajectories, even at the scale achieved, remains fundamentally a problem of sophisticated interpolation-a polished mimicry of observed patterns. The true challenge lies not in reproducing motion, but in understanding its intent. Future iterations must move beyond the passive consumption of visual and linguistic data, and actively model the underlying physical principles governing human interaction. The current reliance on large datasets, while yielding empirical gains, obscures the need for provable, geometrically consistent representations of hand-object dynamics.

A critical limitation is the implicit assumption of a stationary world. Human action rarely unfolds in isolation; environments are dynamic, and interactions are often collaborative. Extending this framework to accommodate multi-agent reasoning, and to predict trajectories contingent upon the actions of others, demands a fundamentally different architectural approach-one grounded in game-theoretic principles rather than purely predictive modeling. The elegance of a solution will not be measured by its accuracy on benchmark datasets, but by its ability to generalize to novel, unforeseen scenarios.

Ultimately, the pursuit of 6DoF trajectory prediction should not be viewed as an end in itself. The ultimate goal is not simply to forecast movement, but to create machines capable of intentional action-agents that can manipulate their environment with purpose and foresight. This necessitates a shift in focus, from statistical correlation to causal inference, and a commitment to the development of algorithms that are not merely accurate, but correct in the most rigorous mathematical sense.

Original article: https://arxiv.org/pdf/2512.16907.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-20 17:09