# GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency

arXiv: https://arxiv.org/abs/2308.11617 · HTML: https://mlsanity.com/paper/2308.11617/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 63 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 30 of 31)

- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation](https://mlsanity.com/paper/2204.13662/) (2022)
- [BEHAVE: Dataset and Method for Tracking Human Object Interactions](https://mlsanity.com/paper/2204.06950/) (2022)
- [First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations](https://mlsanity.com/paper/1704.02463/) (2017)
- [Stochastic Scene-Aware Motion Prediction](https://mlsanity.com/paper/2108.08284/) (2021)
- [ContactPose: A Dataset of Grasps with Object Contact and Hand Pose](https://mlsanity.com/paper/2007.09545/) (2020)
- [Grasping Field: Learning Implicit Representations for Human Grasps](https://mlsanity.com/paper/2008.04451/) (2020)
- [A System for General In-Hand Object Re-Orientation](https://mlsanity.com/paper/2111.03043/) (2021)
- [Populating 3D Scenes by Learning Human-Scene Interaction](https://mlsanity.com/paper/2012.11581/) (2020)
- [Hand-Object Contact Consistency Reasoning for Human Grasps Generation](https://mlsanity.com/paper/2104.03304/) (2021)
- [ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging](https://mlsanity.com/paper/1904.06830/) (2019)
- [GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping](https://mlsanity.com/paper/2112.11454/) (2021)
- [Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction](https://mlsanity.com/paper/2004.13449/) (2020)
- [ContactOpt: Optimizing Contact to Improve Grasps](https://mlsanity.com/paper/2104.07267/) (2021)
- [Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes](https://mlsanity.com/paper/2012.05522/) (2020)
- [Generating 3D People in Scenes without People](https://mlsanity.com/paper/1912.02923/) (2019)
- [Capturing Hands in Action using Discriminative Salient Points and Physics Simulation](https://mlsanity.com/paper/1506.02178/) (2015)
- [D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions](https://mlsanity.com/paper/2112.03028/) (2021)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction](https://mlsanity.com/paper/2209.12354/) (2022)
- [A Skeleton-Driven Neural Occupancy Representation for Articulated Hands](https://mlsanity.com/paper/2109.11399/) (2021)
- [Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control](https://mlsanity.com/paper/1908.07423/) (2019)
- [Physics-Based Dexterous Manipulations with Estimated Hand Poses and Residual Reinforcement Learning](https://mlsanity.com/paper/2008.03285/) (2020)
- [Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction](https://mlsanity.com/paper/2204.13998/) (2022)
- [MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze](https://mlsanity.com/paper/2011.11552/) (2020)

## Built on by (most cited first, 27 of 27)

- [OAKINK2: A Dataset of Bimanual Hands-Object Manipulation in Complex Task Completion](https://mlsanity.com/paper/2403.19417/) (2024)
- [THOR: Text to Human-Object Interaction Diffusion via Relation Intervention](https://mlsanity.com/paper/2403.11208/) (2024)
- [InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation](https://mlsanity.com/paper/2509.09555/) (2025)
- [WANDR: Intention-guided Human Motion Generation](https://mlsanity.com/paper/2404.15383/) (2024)
- [TriDi: Trilateral Diffusion of 3D Humans, Objects, and Interactions](https://mlsanity.com/paper/2412.06334/) (2024)
- [Physics-based Scene Layout Generation from Human Motion](https://mlsanity.com/paper/2405.12460/) (2024)
- [MEgoHand: Multimodal Egocentric Hand-Object Interaction Motion Generation](https://mlsanity.com/paper/2505.16602/) (2025)
- [3D Whole-body Grasp Synthesis with Directional Controllability](https://mlsanity.com/paper/2408.16770/) (2024)
- [Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method](https://mlsanity.com/paper/2403.16169/) (2024)
- [HOSIG: Full-Body Human-Object-Scene Interaction Generation with Hierarchical Scene Perception](https://mlsanity.com/paper/2506.01579/) (2025)
- [Text-Driven 3D Hand Motion Generation from Sign Language Data](https://mlsanity.com/paper/2508.15902/) (2025)
- [Joint Diffusion for Universal Hand-Object Grasp Generation](https://mlsanity.com/paper/2409.04560/) (2024)
- [SimGenHOI: Physically Realistic Whole-Body Humanoid-Object Interaction via Generative Modeling and Reinforcement Learning](https://mlsanity.com/paper/2508.14120/) (2025)
- [HandX: Scaling Bimanual Motion and Interaction Generation](https://mlsanity.com/paper/2603.28766/) (2026)
- [Dex2HOI: Dexterous Bimanual Two-Object Interaction Generation](https://mlsanity.com/paper/2605.30444/) (2026)
- [A Versatile and Differentiable Hand-Object Interaction Representation](https://mlsanity.com/paper/2409.16855/) (2024)
- [MOCHI: Motion Enhancement of Collaborative Human-object Interactions](https://mlsanity.com/paper/2606.18243/) (2026)
- [ConGraspXL: Controllable Constraint-Conditioned Dexterous Grasping Motion Synthesis](https://mlsanity.com/paper/2609.16319/) (2026)
- [GenHeld: Generating and Editing Handheld Objects](https://mlsanity.com/paper/2406.05059/) (2024)
- [Speech2UnifiedExpressions: Synchronous Synthesis of Co-Speech Affective Face and Body Expressions from Affordable Inputs](https://mlsanity.com/paper/2406.18068/) (2024)
- [A Survey of Body and Face Motion: Datasets, Performance Evaluation Metrics and Generative Techniques](https://mlsanity.com/paper/2512.09005/) (2025)
- [LEXIS: LatEnt ProXimal Interaction Signatures for 3D HOI from an Image](https://mlsanity.com/paper/2604.20800/) (2026)
- [IMAGIN-4D: Image-Guided Controllable Interaction Generation](https://mlsanity.com/paper/2606.23675/) (2026)
- [DCGrasp: Distance-aware Controllable Grasp Generation](https://mlsanity.com/paper/2606.29924/) (2026)
- [PAMI: Part Anchored Motion for Text to Human-Object Interaction Generation](https://mlsanity.com/paper/2609.38466/) (2026)
- [Exo2EgoHOI: Hand-Object-Interaction Aware Exocentric-to-Egocentric Video Generation](https://mlsanity.com/paper/2609.38615/) (2026)
- [MFlowHOI: Whole-Body Human-Object Interaction Generation via Part-Aware MeanFlow](https://mlsanity.com/paper/2610.16252/) (2026)

## Abstract (arXiv)

Hands are dexterous and highly versatile manipulators that are central to how humans interact with objects and their environment. Consequently, modeling realistic hand-object interactions, including the subtle motion of individual fingers, is critical for applications in computer graphics, computer vision, and mixed reality. Prior work on capturing and modeling humans interacting with objects in 3D focuses on the body and object motion, often ignoring hand pose. In contrast, we introduce GRIP, a learning-based method that takes, as input, the 3D motion of the body and the object, and synthesizes realistic motion for both hands before, during, and after object interaction. As a preliminary step before synthesizing the hand motion, we first use a network, ANet, to denoise the arm motion. Then, we leverage the spatio-temporal relationship between the body and the object to extract two types of novel temporal interaction cues, and use them in a two-stage inference pipeline to generate the hand motion. In the first stage, we introduce a new approach to enforce motion temporal consistency in the latent space (LTC), and generate consistent interaction motions. In the second stage, GRIP generates refined hand poses to avoid hand-object penetrations. Given sequences of noisy body and object motion, GRIP upgrades them to include hand-object interaction. Quantitative experiments and perceptual studies demonstrate that GRIP outperforms baseline methods and generalizes to unseen objects and motions from different motion-capture datasets.