# HUMAN-TCI: Hierarchical Multi-Stream Motion-Aware Network with Torso-Centered Interaction for Text-to-Motion Retrieval

arXiv: https://arxiv.org/abs/2609.34430 · HTML: https://mlsanity.com/paper/2609.34430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [MotionCLIP: Exposing Human Motion Generation to CLIP Space](https://mlsanity.com/paper/2203.08063/) (2022)
- [The KIT Motion-Language Dataset](https://mlsanity.com/paper/1607.03827/) (2016)
- [ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model](https://mlsanity.com/paper/2304.01116/) (2023)
- [TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesis](https://mlsanity.com/paper/2305.00976/) (2023)
- [Synthesis of Compositional Animations from Textual Descriptions](https://mlsanity.com/paper/2103.14675/) (2021)
- [UDE: A Unified Driving Engine for Human Motion Generation](https://mlsanity.com/paper/2211.16016/) (2022)
- [A Cross-Dataset Study for Text-based 3D Human Motion Retrieval](https://mlsanity.com/paper/2405.16909/) (2024)
- [Tri-Modal Motion Retrieval by Learning a Joint Embedding Space](https://mlsanity.com/paper/2403.00691/) (2024)
- [Chronologically Accurate Retrieval for Temporal Grounding of Motion-Language Models](https://mlsanity.com/paper/2407.15408/) (2024)
- [Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language](https://mlsanity.com/paper/2305.15842/) (2023)
- [MoRAG -- Multi-Fusion Retrieval Augmented Generation for Human Motion](https://mlsanity.com/paper/2409.12140/) (2024)
- [Beyond Global Alignment: Fine-Grained Motion-Language Retrieval via Pyramidal Shapley-Taylor Learning](https://mlsanity.com/paper/2601.21904/) (2026)
- [Multi-Modal Motion Retrieval by Learning a Fine-Grained Joint Embedding Space](https://mlsanity.com/paper/2507.23188/) (2025)
- [Joint-Dataset Learning and Cross-Consistent Regularization for Text-to-Motion Retrieval](https://mlsanity.com/paper/2407.02104/) (2024)

## Abstract (arXiv)

Accurate retrieval of human motions is a crucial first step in text-guided human motion modeling and synthesis, as it selects semantically relevant sequences from large datasets and provides grounded references for downstream tasks. Retrieving motions from natural language descriptions remains challenging because sentences can describe multiple actions, overlapping movements, and intricate dependencies between body parts. Existing methods often focus on simple, single-action descriptions and typically process body parts independently or by merely concatenating features, without explicitly modeling how torso movements influence other parts. In addition, their processing pipelines often rely on computationally heavy models, introducing considerable overhead, particularly when modeling longer or more complex motion sequences. This limits learning discriminative motion-pattern representations, reducing retrieval accuracy, interpretability, and efficiency in practical applications. To address these limitations, we propose HUMAN-TCI, a Hierarchical Multi-Stream Motion-Aware Network for text-guided human motion retrieval. HUMAN-TCI employs a three-stream architecture that separately models upper-body, lower-body, and torso motions while explicitly capturing their interactions, allowing torso-related movements to influence the positioning and dynamics of other body parts. By incorporating tailored torso attention, our model effectively recognizes complex human motion patterns, captures fine-grained motion relationships and handles complex multi-action descriptions. Our framework supports retrieval for both simple, single-action sentences and long, compositional descriptions containing sequential or overlapping actions without relying on complex models.