HOI-Aware Motion Representation

• HOI-aware motion representation is a structured encoding that captures joint human-object dynamics with fine-grained part-level details and contact signals.
• It leverages graph-based models, distance tensors, and mixed embeddings to ensure physical plausibility and temporal consistency in motion synthesis.
• Applications span video editing, VR, and robotics, with empirical benchmarks demonstrating improved realism and reduced collisions.

Hand–Object-Interaction (HOI)-aware motion representation refers to structured encodings, architectures, and learning objectives designed to capture, condition, and generate realistic motion signals that tightly couple human actions with manipulated or contacted objects over time. Such representations provide explicit signals relating the spatial, kinematic, and sometimes semantic relationships between human body parts and object geometry, and are central to modern generative models in video editing, motion synthesis, and physics-aware animation.

1. Core Principles of HOI-aware Motion Representations

HOI-aware motion representations encode the joint motion of human and object in a manner that emphasizes interaction-specific context. These representations differ from traditional human motion models or naive object trackers by explicitly modeling coupling via spatial proximity, contact signals, semantic part assignment, physical constraints, or graph-based affordance structure.

Key principles include:

• Part-level Granularity: Encoding not only whole-body or object trajectories but subdividing human (e.g., hands, arms) and object (e.g., handles, surfaces) geometry into parts to represent fine-grained interaction affordances (Zhang et al., 10 Dec 2025, Li et al., 8 Jun 2025).
• Contact Awareness: Integrating binary or soft-contact maps, interactive distance fields, or anchor-based point correspondences to directly encode moments and regions of human–object contact (Li et al., 18 Jun 2025, Xue et al., 26 Mar 2025).
• Physical and Functional Plausibility: Enforcing constraints or losses rooted in physics (e.g., torque models, non-penetration, inertia, mechanical advantage) to ensure dynamic realism (Wang et al., 8 Aug 2025, Zhao et al., 2023).
• Temporal Consistency: Designing models and representations that are inherently sequential—supporting not just static interaction but the evolution of contact, occlusion, and grasp patterns over time (Xue et al., 11 Jun 2024, Yang et al., 17 Jul 2024).
• Multi-modality and Conditioning: Fusing multiple signals—text, vision, trajectory, depth, and part-assignment—to condition motion generation and guide models toward desired interaction semantics (Yan et al., 1 Dec 2025, Dang et al., 3 Jun 2025).

2. Model Architectures and Encoding Strategies

A spectrum of architectural paradigms supports HOI-aware motion representation:

• Graph-based Structures: Bipartite graphs encode relative movement between human and object parts, where node features include positions/velocities and edges encode geometric and semantic trends (e.g., stationary, approaching, receding) (Deng et al., 24 Mar 2025). The Part Affordance Graph (PAG) formalizes nodes as specific object/human parts with directed contact and motion attributes, extracted from LLMs (Li et al., 8 Jun 2025).
• Distance and Contact Tensors: Interactive Distance Fields (IDF) represent per-frame squared distances between all selected human joints and object keypoints, encoding both contact and avoidance of penetration (Xue et al., 26 Mar 2025).
• Dense and Sparse Masking: Color-encoded masks (sparse trajectory and dense part-level) represent body part and object motion for each frame, directly informing diffusion backbones via FiLM modulation and gating (Zhang et al., 10 Dec 2025). Contact-augmented contour maps combined with depth encode 2D structure and contact regions for scalable, differentiable supervision (Yan et al., 1 Dec 2025).
• Tokenized and Mixed Embeddings: Mixed-representation architectures combine continuous 6DoF global pose (SE(3)) streams with discrete local motion tokens (via LFQ-VAE) to capture both macroscopic movement and fine micro-articulations (Geng et al., 19 May 2025). Contrastive VAEs build continuous HOI token spaces that maintain contact consistency and separate plausible from implausible motions (Geng et al., 21 Mar 2025).
• Explicit Warping and Motion-point Splatting: Warping operators, driven by dense or sparse sets of tracked motion points, inform the propagation of edited content framewise and provide controllable degrees of temporal alignment in editing or synthesis tasks (Xue et al., 11 Jun 2024).
• Multi-view and 4D Alignment: Representations such as the "motion pseudo-video" parameterize point tracks across multiple synchronized camera views and temporal steps, refined post-diffusion for 4D metric alignment (Dang et al., 24 Nov 2025).

3. Integration into Generative and Predictive Pipelines

Modern HOI-aware representations serve as direct inputs, auxiliary signals, or joint supervision targets in diffusion-based and autoregressive conditional generative models:

• Transformer Diffusion and Joint Video/Motion Models: Tri-modal fusion (text/appearance/motion) via adaptive modulation and unified full-attention layers enables simultaneous denoising and generation of videos and explicit motion sequences, facilitating synchrony and dynamic plausibility (Dang et al., 3 Jun 2025).
• Graph- and Attention-based Fusion: Cross-attention modules explicitly fuse contact or trajectory-derived features into diffusion layers, enabling temporally and spatially consistent conditioning at every transformer block (Li et al., 18 Jun 2025, Zhang et al., 10 Dec 2025).
• Reward and Policy-Driven Control: In reinforcement learning pipelines, HOI-aware representations parameterize the reward structure (via RMD or PAG) or directly serve as goal descriptors for policy networks, optimizing for both physical realism and semantic adherence (Deng et al., 24 Mar 2025, Li et al., 8 Jun 2025).
• Closed-loop Correction and Refinement: Joint denoising cycles and alignment losses (e.g., share-and-specialize architectures, multi-view coupling, or VID feedback) propagate gradients across modalities for mutual enhancement of visual realism, multi-view consistency, and physical plausibility (Yan et al., 1 Dec 2025, Dang et al., 24 Nov 2025).
• Physics- and Contact-aware Losses: Supervision includes entity-specific losses (contact agreement, hand agreement, subject consistency), torque or inverse-kinematics penalties, Chamfer distance alignment for point clouds, and explicit contact or velocity discrepancy terms (Xue et al., 11 Jun 2024, Wang et al., 8 Aug 2025, Xue et al., 26 Mar 2025).

4. Comparative Analysis and Empirical Evaluation

Empirical studies, reflected in recent SOTA benchmarks, highlight the impact of HOI-aware representations:

• Part-aware and contact-based methods consistently outperform instance- or centroid-only approaches in realism, semantic alignment (VideoCLIP, R-Precision), and physical plausibility (foot sliding, collision percentage, physical plausibility score) (Zhang et al., 10 Dec 2025, Xue et al., 26 Mar 2025).
• Dense mask and IDF-based losses capture multi-contact affordances, reducing collision and increasing contact stability relative to prior methods (Yan et al., 1 Dec 2025, Xue et al., 26 Mar 2025).
• Ablation studies confirm that removing part-level or contact signals degrades semantic alignment, increases collision/interpenetration, and yields more "hallucinated" or ungrounded interactions (Zhang et al., 10 Dec 2025, Yan et al., 1 Dec 2025, Xue et al., 26 Mar 2025).
• Human evaluations report that affordance- and contact-guided models are strongly preferred (>60–90%) for realism and motion adherence (Zhang et al., 10 Dec 2025, Li et al., 8 Jun 2025).

5. Application Domains and Impact

HOI-aware motion representations are foundational in domains where tight, dynamic coupling of humans and objects is critical:

• Video Editing and Synthesis: HOI-aware diffusion models enable realistic re-synthesis, object swapping, and manipulation in both in-domain and open-world settings (Xue et al., 11 Jun 2024, Yan et al., 1 Dec 2025).
• Virtual Reality and Animation: Enhanced interaction realism, personalized affordances, and semi-automated control for avatars and scene agents (Deng et al., 24 Mar 2025, Geng et al., 19 May 2025).
• Robotics and Manipulation: Bridging perception-driven policy design with semantically controllable and physically grounded planning (Li et al., 8 Jun 2025, Wang et al., 8 Aug 2025, Zhao et al., 2023).
• Dataset Construction and Benchmarking: New motion and video datasets provide granular annotation of object attributes and contact events, insightful for analysis and training of next-generation models (Wang et al., 8 Aug 2025, Yang et al., 17 Jul 2024).

6. Open Challenges and Future Directions

Despite progress, several limitations and open research directions remain:

• Generalization Across Objects and Scenes: Addressing limited anthropometric diversity, surface geometries, and interaction modes to ensure robust transfer to unseen settings (Li et al., 18 Jun 2025, Yan et al., 1 Dec 2025).
• Physical Simulation Integration: Moving beyond simplified torque or contact proxies to true physical simulation (Newton–Euler), enabling more accurate modeling of force, compliance, and causality (Wang et al., 8 Aug 2025).
• Multi-modal and Multi-agent Scaling: Extending representations to incorporate facial expressions, gaze, and complex multi-agent–multi-object coordination (Dang et al., 3 Jun 2025, Li et al., 8 Jun 2025).
• Efficient Sparse-to-Dense Control: Further research into trajectory densification and compact conditional representations for more efficient, user-controllable synthesis (Zhang et al., 10 Dec 2025).
• Interpretability and Diagnostics: Developing new visualization and diagnostic tools for part-level contact, force tracing, and failure identification in an HOI context.

7. Representative Methods and Quantitative Benchmarks

Method	Representation Type	Key Innovation	Notable Metric(s)	Reference
VHOI	Part-aware sparse/dense masks	Palette-based color masks + FiLM fusion	FVD=915, CA=0.827	(Zhang et al., 10 Dec 2025)
HOI-PAGE	Part Affordance Graph (PAG)	LLM-derived semantic graph for constraints	non-collision 0.99, contact 0.92	(Li et al., 8 Jun 2025)
GenHOI	Sparse 3D keyframes + contact	Contact-aware encoder + cross-attention	Outperforms on OMOMO/3D-FUTURE	(Li et al., 18 Jun 2025)
ROG	Interactive Distance Field (IDF)	24×24 joint–keypoint grids, guided diffusion	R-P@1=0.706, FID=5.119	(Xue et al., 26 Mar 2025)
SCAR	Contact–augmented contour + depth	Efficient 2D, differentiable dual-channel rep.	+0.3 total VBench score	(Yan et al., 1 Dec 2025)

These results illustrate the decisive effect of intentional HOI-aware encoding over naively human- or object-centric methods in generative quality and physical fidelity.