MoVLR: Motion from Vision-Language Representation

• Motion from Vision-Language Representation (MoVLR) is a framework that unifies vision, language, and motion to infer trajectories, generate video sequences, and plan actions.
• Key techniques include multimodal alignment using models like CLIP, explicit motion parameterization (e.g., bounding-box chains), and end-to-end differentiable training with contrastive and generative losses.
• Applications span video synthesis, multi-agent robotic planning, and behavioral understanding, while addressing challenges like data scarcity and cross-modal transfer.

Motion from Vision-Language Representation (MoVLR) refers to the methodologies and frameworks by which motion—of agents, objects, or articulated bodies—is inferred, parameterized, planned, or generated directly from joint visual and linguistic input representations. MoVLR unifies visual observations (images, videos, or skeletons) with human-provided language (goals, descriptions, or instructions) to produce motion-centric outputs, including trajectory predictions, action sequences, or explicit video/motion synthesis. Recent frameworks formalize MoVLR as a structured interface or intermediate that facilitates reasoning, planning, behavior understanding, and controllable generation in both simulation and physically grounded domains.

1. Core Methodological Principles of MoVLR

MoVLR systems fundamentally link three modalities: vision, language, and motion/action. Implementations diverge, but the key principles are:

• Multimodal Representation Alignment: Visual and textual features are projected into shared or closely coupled embedding spaces, often using pre-trained vision-LLMs (e.g., CLIP, SigLIP, LanguageBind), enabling cross-modal understanding and grounding (Gupta et al., 13 Aug 2025, Fan et al., 4 Nov 2025).
• Structured Motion Parameterization: Motion is encoded via explicit (trajectories, optical flow, motion patches) or implicit (discrete codebooks, quantized tokens) structures. Techniques include bounding-box chains (Yang et al., 1 Oct 2025), per-pixel flow fields (Ranasinghe et al., 12 May 2025), part-aware codes (Cao et al., 11 Aug 2025), and unified token vocabularies (Fan et al., 4 Nov 2025).
• End-to-End Differentiable Learning: MoVLR architectures are typically trained in an end-to-end fashion, often using a combination of reconstruction, contrastive, generative (diffusion), or next-token objectives, reinforced by multimodal data (Lin et al., 10 Dec 2025, Cao et al., 11 Aug 2025, Huang et al., 21 Oct 2025).
• Physical and Semantic Consistency: Recent methods incorporate explicit physics priors (e.g., heat equation for collision avoidance (Chae et al., 15 Dec 2025), physical-constraint loss terms (Yang et al., 1 Oct 2025)), or iteratively refine policy and reward using VLM feedback for biomechanical validity (Soedarmadji et al., 28 Dec 2025).

2. Architectures and Representations

A wide architectural diversity characterizes current MoVLR systems. Key instantiations include:

Framework	Visual Encoder	Language Interface	Motion Representation	Action/Generation Head
TrajVLM-Gen (Yang et al., 1 Oct 2025)	SigLIP2	Qwen2.5-8B	Bounding-box chain-of-thought	OpenSora diffusion, trajectory-masked attn
XR-1 (Fan et al., 4 Nov 2025)	SigLIP + ViT	Transformer tokens	Discrete UVMC (VQ-VAE)	Gemma-based transformer
HiF-VLA (Lin et al., 10 Dec 2025)	DINOv2 + SigLIP	Multimodal	2D macroblock motion vectors; GOP window	Prismatic-7B+joint expert
LCHD (Chae et al., 15 Dec 2025)	CLIP (ViT-B/32)	CLIP text embedding	Score field (∇ₓ log pₜ(x)) in workspace	Physics-inspired diffusion, cross-attn
Being-M0.5 (Cao et al., 11 Aug 2025)	SigLIP	LLaMA-2-7B	Part-aware residual quantized tokens	7B LLM autoregressive + PRQ decoder

Motion tokens may be continuous (trajectories, flows), discrete (codebook indices, decomposed symbolic actions), or hybrid embeddings, often dependent on dataset and downstream use-case. Extraction pipelines include VQ-VAE-style vector quantization (Fan et al., 4 Nov 2025, Cao et al., 11 Aug 2025), CLIP-style dual encoders (Yu et al., 2024), or autoregressively emitted numeric sequences (Yang et al., 1 Oct 2025).

3. Training Objectives and Loss Functions

Training strategies integrate the following loss families:

• Prediction Losses: Direct regression or generation of future bounding-boxes, motion tokens, vectors (Yang et al., 1 Oct 2025, Lin et al., 10 Dec 2025).
• Contrastive or Alignment Losses: CLIP-style symmetric contrastive loss in cross-modal latent space, e.g., for motion patches and language (Yu et al., 2024), or VQ-VAE code alignment (Fan et al., 4 Nov 2025).
• Autoregressive Cross-Entropy: Next-token likelihood for LLM-driven models (particularly in sequence generation), optionally including vision and motion tokens (Cao et al., 11 Aug 2025, Gupta et al., 13 Aug 2025).
• Physics/Reward-based Objectives: Losses encoding physical feasibility, collision, reachability, or reward shaping, either via differentiable priors (heat kernel (Chae et al., 15 Dec 2025), smoothness/collision costs (Wu et al., 17 Mar 2025)), or by coupling rollout quality to VLM feedback scores (Soedarmadji et al., 28 Dec 2025).

Each method may employ auxiliary objectives (e.g., AdaLN history embedding (Lin et al., 10 Dec 2025)) or training phases (e.g., XR-1’s three-stage paradigm: UVMC VQ-VAE, cross-embodiment VLM transfer, task-specific fine-tuning (Fan et al., 4 Nov 2025)).

4. Applications and Empirical Results

MoVLR techniques support diverse applications:

• Trajectory Forecasting & Video Generation: TrajVLM-Gen produces physically consistent object trajectories and controls generation by converting predicted boxes into cross-attention masks. Achieves FVD 545 on UCF-101, outperforming prior SOTA by 14 points (Yang et al., 1 Oct 2025).
• Multi-Agent Robotic Motion Planning: LCHD learns language-to-trajectory planning over images with heat-inspired diffusion, enabling ~100% success and OOD robustness in navigation and real-robot settings (Chae et al., 15 Dec 2025).
• Vision-Language Behavioral Understanding: ViMoNet’s MoVLR features achieve +39.4% improvement over GPT-3.5 baselines on composite motion-linguistic reasoning (Gupta et al., 13 Aug 2025).
• Real-Time Motion Generation and Control: Being-M0.5 exploits PRQ for part-specific motion code control, sustaining ≥20 FPS and yielding SOTA on text-to-motion R@1, FID, and multi-task curricula (Cao et al., 11 Aug 2025).
• Generalization and Embodiment Transfer: XR-1 demonstrates robust transfer across objects, scenes, robots, and lighting, with high OOD task success driven by UVMC consolidation (Fan et al., 4 Nov 2025).
• Reward Discovery in Control: MoVLR for musculoskeletal and manipulation control integrates VLMs/LLMs in-the-loop to refine rewards, achieving error reductions and biomechanical plausibility unattainable by hand-crafted designs (Soedarmadji et al., 28 Dec 2025).

5. Motion Representation Taxonomy and Comparative Insights

Motion-from-Vision-Language pipelines leverage various encoding schemes:

• Explicit Parameterizations: Bounding-box chains (Yang et al., 1 Oct 2025), SE(3) waypoints (Wu et al., 17 Mar 2025), macroblock motion vectors (Lin et al., 10 Dec 2025).
• Image-Space Flows: Dense per-pixel optical flow as universal intermediate, decoupling policy from explicit 3D annotations (Ranasinghe et al., 12 May 2025, Qian et al., 2022).
• Discrete Token Vocabularies: UVMC (XR-1) or PRQ (Being-M0.5), enabling efficient alignment and instruction-driven part-level control.
• Hybrid / Decomposed Representations: Symbolic textual tokens (MoTVLA) for fast-slow motion decomposition; joint vision-language embedding spaces for behavioral inference (ViMoNet, “motion patches” (Yu et al., 2024)).

In general, discrete tokenization approaches (VQ-VAE, PRQ) offer scalability and compositionality across diverse datasets and embodiments, while flow- and vector-based methods provide dense, interpretable grounding but require more structured mapping for down-streaming to action policies.

6. Datasets, Scalability, and OOD Generalization

Progress in MoVLR is closely tied to dataset availability and diversity:

• Human Motion: HuMo100M (Being-M0.5) at 5M sequences, VIMOS (ViMoNet), Motion-X.
• Robotic/Embodiment-agnostic: XR-D, OpenX, RoboMIND (XR-1), MetaWorld and real-world table-top (Ranasinghe et al., 12 May 2025).
• Tracked Trajectory Datasets: TNL2K, LaSOT, GOT-10k for bounding-box chain prediction (Yang et al., 1 Oct 2025).

Empirical evidence consistently shows that models explicitly disentangling or aligning vision, language, and motion are more robust under novel conditions. For example, heat-kernel diffusion in LCHD strictly blocks planning to unreachable semantic goals, and UVMC tokens in XR-1 enable transfer to distinct robot types and household environments. Part-aware quantization in Being-M0.5 affords fine-grained, instruction-driven articulation.

7. Limitations and Future Directions

Despite rapid advances, current MoVLR approaches face limitations:

• Data Bottlenecks: Motion-text paired data is several orders of magnitude smaller than image-text; pretraining and dataset expansion remain active areas (Yu et al., 2024, Gupta et al., 13 Aug 2025).
• Depth/Occlusion/3D Reasoning: Most flow-based and token-discretization approaches operate in 2D or low-resolution 3D; rich scene understanding (including occlusions) requires further work (Ranasinghe et al., 12 May 2025).
• Inference Latency: Real-time requirements are now addressed (≥20 FPS (Cao et al., 11 Aug 2025)), but diffusion-based models and large LLM policies remain computationally intensive for some control settings (Yang et al., 1 Oct 2025).
• Transfer and Compositionality: While discrete codes enhance transfer, bridging across highly distinct agent morphologies and behaviors is nontrivial; more explicit alignment of world models may be needed (Fan et al., 4 Nov 2025).
• Explanatory Reasoning: Some pipelines optimize for metric performance rather than interpretable plans, although chain-of-thought and instruction-based outputs are an emerging trend (Yang et al., 1 Oct 2025, Huang et al., 21 Oct 2025).

A plausible implication is continued cross-fertilization between foundation model scaling, structured token vocabularies, physics-based priors, and hierarchical planners will drive the next stage of unified MoVLR frameworks—enabling ever more general, robust, and controllable systems across human, agent, and robotic domains.