AgentHOI: Multi-Agent Reasoning for Human-Object-Interaction Video Generation via Implicit Representation Alignment

2026-07-27 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

AgentHOI is a novel text-driven framework for human-object interaction (HOI) video generation, addressing limitations of explicit motion control. It employs a "thinking-before-generation" schema, utilizing multi-agent reasoning for visual perception, interaction analysis, and motion planning to translate high-level textual intent into physically grounded action timelines. The system integrates an implicit text-to-motion alignment strategy, distilling motion priors into its Wan2.1-I2V-14B video diffusion model, which removes the need for explicit motion inputs during inference. AgentHOI's training leverages a mixed-source dataset of 71,000 real-world and 37,000 synthetic clips, with synthetic data applied at high-noise timesteps. Experiments demonstrate superior performance over state-of-the-art methods in interaction naturalness, object appearance preservation (Obj-DINO), and adherence to complex instructions across cross-motion-driven, HOMA, and AnchorCrafter test sets, with user studies showing significant preference.

Key takeaway

For Machine Learning Engineers developing controllable human-object interaction video generation, AgentHOI demonstrates a robust path to text-driven control. You should consider integrating multi-agent reasoning to deconstruct complex interactions into explicit action plans. Furthermore, adopting implicit text-to-motion feature alignment can embed crucial motion dynamics into your diffusion models, reducing reliance on expensive explicit motion inputs at inference. This strategy significantly improves interaction naturalness and object consistency, enabling more scalable and generalizable HOI synthesis.

Key insights

Text-driven HOI video generation benefits from multi-agent reasoning and implicit text-motion alignment, eliminating explicit motion inputs.

Principles

• Deconstruct complex HOI tasks into physically grounded execution blueprints.
• Distill text-to-motion priors into video models for robust synthesis.
• Combine real and synthetic data to enhance diversity and generalization.

Method

AgentHOI uses a multi-agent system (perception, relation, motion planner, clipping refiner) to create a second-by-second action timeline from text. This plan conditions a Wan2.1-I2V-14B diffusion model, which is trained with implicit text-to-motion feature alignment using TRD loss.

In practice

• Use multi-agent reasoning to bridge high-level intent and physical execution.
• Implement implicit feature alignment to embed motion priors into diffusion models.
• Curate hybrid datasets (real + synthetic) for diverse interaction learning.

Topics

• Human-Object Interaction
• Video Generation
• Multi-Agent Reasoning
• Diffusion Models
• Text-to-Motion Alignment
• Hybrid Data Pipelines

Code references

• bone-11/agenthoi

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Machine Learning Engineer

Related on AIssential

• AgentHOI: Multi-Agent Reasoning for Human-Object-Interaction Video Generation via Implicit Representation Alignment — AgentHOI uses multi-agent reasoning and implicit text-motion alignment for robust, text-driven HOI video generation without explicit motion inputs.
• SparseCtrl-HOI: Sparse Temporal Control for Human-Object Interaction Video Generation — Sparse temporal control with MLLM-driven motion priors enables realistic HOI video generation with reduced annotation.
• Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework — A new benchmark and agentic self-correcting framework using I2V models significantly improve complex Human-Object Interaction (HOI) image editing.
• StreamHOI: Interaction-aware Temporal Memory Adaptation for Streaming HOI Video Generation — StreamHOI adapts Transformer block memory based on spatial attention bias for consistent, low-latency, long-duration HOI video generation.
• Making Foresight Actionable: Repurposing Representation Alignment in World Action Models — Aligning visual representations with action-grounded semantics improves robot manipulation policies in World Action Models.
• MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks — MAVEN's agentic, multi-stage pipeline automates high-quality, domain-adaptable video annotation for VLM training.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.