From Words to Movement: A New Path for Realistic Motion Capture

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Author: Denis Avetisyan

Researchers have developed a framework that translates natural language descriptions into lifelike human movements, offering a significant advance over video-based approaches.

The system aligns language, geometric motion, and visual appearance within a shared embedding space-facilitated by training on real-world videos and point trajectories-to decode text descriptions and initial grid masks into physically plausible trajectory sequences, effectively generating motion without requiring video synthesis.
The system aligns language, geometric motion, and visual appearance within a shared embedding space-facilitated by training on real-world videos and point trajectories-to decode text descriptions and initial grid masks into physically plausible trajectory sequences, effectively generating motion without requiring video synthesis.

Lang2Motion establishes a joint embedding space aligned with CLIP, enabling the generation of high-quality point trajectories from textual prompts.

Generating realistic and controllable motion remains a challenge despite advances in video synthesis and human pose estimation. This paper introduces Lang2Motion: Bridging Language and Motion through Joint Embedding Spaces, a novel framework that generates point trajectories from natural language by aligning motion representations with the CLIP vision-language embedding space. This approach achieves superior performance on text-to-trajectory retrieval and motion accuracy compared to video-based methods, while also demonstrating effective transfer across diverse motion domains. Could this paradigm shift unlock new possibilities for intuitive motion control and semantic manipulation in robotics, animation, and beyond?

The Semantic-Kinetic Disconnect: A Fundamental Challenge

Current video and language processing technologies frequently fall short when tasked with replicating the subtleties of human movement. Traditional models often treat motion as a purely visual phenomenon, or a series of joint angles, neglecting the rich semantic information embedded within actions. This results in generated motions that appear robotic, lack natural variation, or fail to accurately reflect the intent expressed in accompanying language. Capturing the full spectrum of human physicality – the slight hesitations, the expressive gestures, and the nuanced timing – proves particularly challenging. Consequently, systems struggle to create convincingly realistic or controllable animations, highlighting a significant gap in the ability to bridge the semantic world of language with the dynamic complexities of physical performance.

Current approaches to synthesizing human motion from language frequently stumble when translating abstract semantic concepts into physically plausible actions. The core difficulty lies in the vast disconnect between the descriptive power of language and the intricate geometric constraints governing bodily movement. A statement like “walk confidently” encapsulates a complex interplay of gait, posture, and velocity, yet many systems treat these elements as independent variables, failing to capture the subtle correlations crucial for realism. This often results in motions that are either unnatural – exhibiting jerky movements or impossible poses – or lack the nuanced expressiveness intended by the linguistic input. Bridging this gap necessitates models capable of understanding not just what action is requested, but also how that action manifests geometrically within the constraints of biomechanics and physics; a challenge that demands a more holistic integration of semantic understanding with dynamic simulation.

Truly realistic motion generation demands more than simply translating words into action; it necessitates a deep understanding of the interplay between semantic content and kinetic expression. A system capable of bridging this gap must discern not only what an action is – walking, jumping, or waving – but also how that action is performed, factoring in subtleties like speed, force, and emotional context. This requires a model that moves beyond purely geometric representations of movement and incorporates an understanding of the intent and nuance embedded within language, allowing for a level of control and expressiveness previously unattainable. Effectively, the system must learn to interpret linguistic cues as instructions for a dynamic performance, mirroring the complex relationship between speech and bodily behavior observed in humans.

Lang2Motion excels at generating temporally coherent, coordinated multi-subject motions-such as realistic football gameplay-unlike methods like MotionCLIP, which prioritizes pose accuracy over temporal flow and can produce contextually inappropriate actions, and T2M-GPT, which struggles with both pose and multi-object scenes.
Lang2Motion excels at generating temporally coherent, coordinated multi-subject motions-such as realistic football gameplay-unlike methods like MotionCLIP, which prioritizes pose accuracy over temporal flow and can produce contextually inappropriate actions, and T2M-GPT, which struggles with both pose and multi-object scenes.

Lang2Motion: A Framework for Semantic Trajectory Synthesis

Lang2Motion employs the CLIP (Contrastive Language-Image Pre-training) model to establish a unified embedding space for both textual descriptions and 3D point trajectories. This is achieved by mapping both modalities into a common vector space, where semantic similarity dictates proximity. Specifically, the system learns to represent a trajectory and its corresponding textual description with vectors close to each other in this space. Consequently, manipulating the textual description – for example, altering adjectives or verbs – directly influences the generated trajectory, enabling semantic control over the resulting motion. This approach allows users to guide trajectory generation not through explicit coordinates, but through high-level linguistic commands, facilitating intuitive and expressive motion design.

The Lang2Motion framework employs a Transformer Encoder to project both 3D point trajectories and trajectory overlays into the embedding space defined by the Contrastive Language-Image Pre-training (CLIP) model. This encoding process transforms the kinematic data into a vector representation compatible with textual descriptions also embedded within the CLIP space. Specifically, the Transformer Encoder learns to map the spatial and temporal characteristics of motion to a latent representation, enabling a direct comparison and alignment between visual movement and linguistic commands. This alignment is crucial for achieving semantic control, as it allows the system to interpret textual prompts and generate corresponding trajectories based on learned associations between language and motion characteristics.

The Transformer Decoder in Lang2Motion functions by autoregressively predicting future trajectory points based on the encoded latent representation and previously generated points. This process utilizes masked self-attention to ensure that predictions are conditioned only on past trajectory information, maintaining temporal coherence. The decoder is trained to minimize the L2 distance between predicted and ground truth trajectory points, enabling the generation of smooth and realistic motion sequences. By varying the input text embedding, the system demonstrates controllable trajectory generation, as the decoder reconstructs trajectories aligned with the semantic content of the provided language description.

Lang2Motion utilizes a transformer encoder-decoder architecture to generate trajectory sequences from text descriptions by aligning trajectory overlays with CLIP text and image embeddings, and then reconstructing trajectories autoregressively based on latent representations and previous positions.
Lang2Motion utilizes a transformer encoder-decoder architecture to generate trajectory sequences from text descriptions by aligning trajectory overlays with CLIP text and image embeddings, and then reconstructing trajectories autoregressively based on latent representations and previous positions.

Dual Supervision: Aligning Language and Visual Ground Truth

Lang2Motion utilizes a dual supervision approach during model training, incorporating two distinct data modalities: natural language descriptions of desired motions and visual data in the form of trajectory overlays. These trajectory overlays are rendered motion trails directly applied to video frames, providing the model with explicit visual cues regarding the intended movement paths. By simultaneously learning from both textual instructions and corresponding visual representations of motion, the model benefits from complementary information sources. This allows Lang2Motion to establish a stronger correlation between language and visual movement, ultimately improving the quality and controllability of generated motions.

Trajectory overlays function as a visual aid for the Contrastive Language-Image Pre-training (CLIP) model, directly exposing it to representations of motion within video sequences. By rendering motion trails – the overlays – on training video frames, the system provides CLIP with explicit data regarding the paths of moving objects. This augmentation enhances CLIP’s ability to correlate textual descriptions with observed motion patterns, leading to improvements in both the accuracy – how closely generated trajectories match intended movements – and the coherence – the naturalness and fluidity of the motion – of the resulting trajectories. The overlays effectively bridge the semantic gap between language and visual motion data, enabling more precise alignment during the training process.

Trajectory reconstruction within Lang2Motion is optimized through the combined application of L1 and L2 loss functions. The L1 loss, calculated as the mean absolute error between predicted and ground truth trajectory points, prioritizes accurate point-to-point correspondence. Simultaneously, the L2 loss, representing the squared Euclidean distance, penalizes deviations in trajectory smoothness. This dual-loss approach effectively balances fidelity to the reference motion – minimizing positional errors – with the generation of natural, fluid movements, preventing jagged or unrealistic motion paths. The weighting of these loss functions is tuned to achieve an optimal trade-off between accuracy and smoothness during model training.

Grid Initialization establishes preliminary trajectories by discretizing the video frame into a grid and assigning initial motion points to each cell; this provides a foundational spatial understanding for the model. These initial trajectories, however, are subject to refinement throughout the Lang2Motion framework via iterative optimization processes. The framework leverages loss functions and dual supervision signals – textual descriptions and trajectory overlays – to adjust and improve the accuracy and coherence of these points, ultimately generating more realistic and fluid motion sequences. This iterative refinement ensures the final trajectories are not solely dependent on the initial grid structure but are guided by both semantic understanding and visual data.

Lang2Motion generates diverse motion sequences from the same text prompt (
Lang2Motion generates diverse motion sequences from the same text prompt (“dancing panda”) by varying the initial grid position, effectively demonstrating semantic interpolation within CLIP’s embedding space and allowing control over whether the generated sequence emphasizes motion or appearance.

Generalization and a New Benchmark in Motion Synthesis

Rigorous testing of Lang2Motion on the NTU RGB+D and Kinetics-Skeleton datasets confirms its robust capacity for generalization to previously unseen human actions. This ability stems from the framework’s design, which prioritizes understanding the semantic meaning of language prompts rather than simply memorizing training data. Consequently, Lang2Motion doesn’t require specific examples of an action to generate a plausible and accurate motion sequence; it can synthesize movements for novel prompts with remarkable fidelity. The performance metrics on these datasets – achieving 88.3% Top-1 accuracy on NTU RGB+D and 41.6% on Kinetics-Skeleton – demonstrate a significant leap in the field, showcasing the potential for creating human motion from language that extends beyond the limitations of previously trained examples and paving the way for broader applications in areas like animation and robotics.

Lang2Motion establishes a new benchmark in human action recognition, demonstrably surpassing existing methodologies through rigorous evaluation on established datasets. Specifically, the framework attains 88.3% Top-1 accuracy on the NTU RGB+D dataset, a significant leap in correctly identifying human actions from visual data. This performance extends to the more complex Kinetics-Skeleton dataset, where Lang2Motion achieves a 41.6% accuracy rate. These results not only highlight the framework’s robust understanding of human movement but also position it as a leading solution for applications requiring precise and reliable action classification, paving the way for advancements in fields like human-computer interaction and motion analysis.

Evaluations demonstrate that Lang2Motion significantly surpasses existing video-based Vision-Language Models (VLMs) in motion prediction accuracy. Specifically, the framework reduces Average Displacement Error (ADE) and Final Displacement Error (FDE) by 33 to 35 percent, indicating a substantial improvement in the precision of predicted motion trajectories. Further bolstering its performance, Lang2Motion achieves an Average Jaccard index of 0.84, a measure of overlap between predicted and ground truth motion, while comparable VLMs only reach scores between 0.42 and 0.45. These results highlight Lang2Motion’s capacity to generate more realistic and accurate human motion from textual prompts, establishing a new benchmark in the field and promising enhanced applications across diverse domains.

Lang2Motion demonstrates a remarkable capacity for zero-shot learning, effectively generating realistic human motions in response to textual prompts it has never encountered during training. This capability is quantified by a Recall@1 score of 34.2%, signifying that, when presented with a novel action request, the framework’s generated motion is among the top predicted results 34.2% of the time – a substantial 12.5% improvement over the performance of comparable video-based vision-language models like X-CLIP. Furthermore, the framework achieves an even more impressive Recall@10 of 84.6%, indicating that a plausible motion sequence appears within the top ten predictions in over 84% of cases, highlighting its robustness and adaptability to unseen actions and its potential for diverse applications requiring dynamic, generated movement.

The output of Lang2Motion isn’t merely data; it’s a faithful representation of human motion captured as precise point trajectories. This fidelity unlocks a diverse spectrum of practical applications, extending far beyond the realm of simple action recognition. Animators can leverage these trajectories to generate realistic and nuanced character movements, streamlining the animation pipeline and enhancing visual storytelling. Furthermore, the data proves invaluable for robotics, offering a foundation for training robots to mimic human actions with greater accuracy and adaptability. The framework’s ability to generate motion data opens doors for virtual and augmented reality experiences, human-computer interaction, and even personalized physical rehabilitation programs, signifying a powerful tool with implications across numerous disciplines.

This pipeline compares video-based trajectory generation using a VLM-which extracts motion from generated video frames-with the direct trajectory generation of Lang2Motion, evaluating both with consistent motion quality metrics.
This pipeline compares video-based trajectory generation using a VLM-which extracts motion from generated video frames-with the direct trajectory generation of Lang2Motion, evaluating both with consistent motion quality metrics.

The Lang2Motion framework, with its emphasis on aligning trajectory representations within the CLIP embedding space, embodies a commitment to foundational mathematical principles. This approach isn’t merely about achieving functional trajectory generation; it’s about establishing a provable correspondence between linguistic input and dynamic movement. As Andrew Ng states, “AI is bananas!” – a playful reminder that even in the realm of complex systems, rigorous grounding in fundamentals remains paramount. The pursuit of a joint embedding space, as demonstrated in the article, isn’t simply about improving performance; it’s about constructing a system where the correctness of the transformation from language to motion can be demonstrated, not merely observed through testing. This focus on provable correctness mirrors a dedication to mathematical purity, ensuring the generated trajectories aren’t just plausible, but logically derived from the provided description.

What’s Next?

The elegance of Lang2Motion lies in its reduction of a complex problem – motion synthesis – to the more tractable space of embedding alignment. However, one should not mistake correlation, however strong, for true understanding. The framework successfully maps language to trajectory, but the underlying dynamics – the why of movement – remain largely unmodeled. Future work must address this; simply generating plausible motion is insufficient. A provably correct representation of physical laws, even approximated, would be a significant advancement, moving beyond superficial realism.

The reliance on CLIP, while pragmatic, introduces a dependency on a pre-trained, general-purpose model. This is, to put it mildly, a compromise. The semantic space of CLIP is vast and largely unrelated to the nuances of human motion. A dedicated embedding space, trained specifically on kinematic and dynamic data, promises a more efficient and less ambiguous representation. Optimization without analysis, as the saying goes, is self-deception; the current approach benefits from CLIP’s breadth but sacrifices precision.

Finally, the generation of point trajectories, while computationally efficient, neglects the richness of articulated motion. Extending this framework to synthesize full body poses, while undoubtedly more complex, represents a natural progression. The true test will not be in generating plausible gaits, but in creating motions that are not merely possible, but intentional – movements that reflect an underlying purpose, a discernible goal.

Original article: https://arxiv.org/pdf/2512.10617.pdf

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

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2025-12-13 23:46