# Diffusion-Guided Reconstruction of Everyday Hand-Object Interaction Clips

arXiv: https://arxiv.org/abs/2309.05663 · HTML: https://mlsanity.com/paper/2309.05663/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- Builds on 78 of its 91 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 30 of 78)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation](https://mlsanity.com/paper/2212.00774/) (2022)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning](https://mlsanity.com/paper/1904.01786/) (2019)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [Shap-E: Generating Conditional 3D Implicit Functions](https://mlsanity.com/paper/2305.02463/) (2023)
- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction](https://mlsanity.com/paper/2203.01577/) (2022)
- [ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation](https://mlsanity.com/paper/2204.13662/) (2022)
- [RealFusion: 360deg Reconstruction of Any Object from a Single Image](https://mlsanity.com/paper/2302.10663/) (2023)
- [Novel View Synthesis with Diffusion Models](https://mlsanity.com/paper/2210.04628/) (2022)
- [Understanding Human Hands in Contact at Internet Scale](https://mlsanity.com/paper/2006.06669/) (2020)

## Built on by (most cited first, 30 of 74)

- [HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video](https://mlsanity.com/paper/2311.18448/) (2023)
- [DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions](https://mlsanity.com/paper/2403.17827/) (2024)
- [G-HOP: Generative Hand-Object Prior for Interaction Reconstruction and Grasp Synthesis](https://mlsanity.com/paper/2404.12383/) (2024)
- [EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild](https://mlsanity.com/paper/2411.14280/) (2024)
- [GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion](https://mlsanity.com/paper/2402.14810/) (2024)
- [Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects](https://mlsanity.com/paper/2403.16428/) (2024)
- [ZeroMimic: Distilling Robotic Manipulation Skills from Web Videos](https://mlsanity.com/paper/2503.23877/) (2025)
- [BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction with Articulated Objects](https://mlsanity.com/paper/2412.05066/) (2024)
- [Reconstructing Hand-Held Objects in 3D from Images and Videos](https://mlsanity.com/paper/2404.06507/) (2024)
- [EgoGaussian: Dynamic Scene Understanding from Egocentric Video with 3D Gaussian Splatting](https://mlsanity.com/paper/2406.19811/) (2024)
- [InterTrack: Tracking Human Object Interaction without Object Templates](https://mlsanity.com/paper/2408.13953/) (2024)
- [DreamActor-H1: High-Fidelity Human-Product Demonstration Video Generation via Motion-designed Diffusion Transformers](https://mlsanity.com/paper/2506.10568/) (2025)
- [Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric Videos](https://mlsanity.com/paper/2405.04370/) (2024)
- [HOI-Swap: Swapping Objects in Videos with Hand-Object Interaction Awareness](https://mlsanity.com/paper/2406.07754/) (2024)
- [Controlling the World by Sleight of Hand](https://mlsanity.com/paper/2408.07147/) (2024)
- [Re-HOLD: Video Hand Object Interaction Reenactment via adaptive Layout-instructed Diffusion Model](https://mlsanity.com/paper/2503.16942/) (2025)
- [DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy](https://mlsanity.com/paper/2506.20668/) (2025)
- [MADiff: Motion-Aware Mamba Diffusion Models for Hand Trajectory Prediction on Egocentric Videos](https://mlsanity.com/paper/2409.02638/) (2024)
- [ManiVideo: Generating Hand-Object Manipulation Video with Dexterous and Generalizable Grasping](https://mlsanity.com/paper/2412.16212/) (2024)
- [MagicHOI: Leveraging 3D Priors for Accurate Hand-object Reconstruction from Short Monocular Video Clips](https://mlsanity.com/paper/2508.05506/) (2025)
- [CARI4D: Category Agnostic 4D Reconstruction of Human-Object Interaction](https://mlsanity.com/paper/2512.11988/) (2025)
- [HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions](https://mlsanity.com/paper/2403.18575/) (2024)
- [LOME: Learning Human-Object Manipulation with Action-Conditioned Egocentric World Model](https://mlsanity.com/paper/2603.27449/) (2026)
- [Do as I Do: Dexterous Manipulation Data from Everyday Human Videos](https://mlsanity.com/paper/2606.19333/) (2026)
- [MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision](https://mlsanity.com/paper/2310.11696/) (2023)
- [InterDyn: Controllable Interactive Dynamics with Video Diffusion Models](https://mlsanity.com/paper/2412.11785/) (2024)
- [HORT: Monocular Hand-held Objects Reconstruction with Transformers](https://mlsanity.com/paper/2503.21313/) (2025)
- [Web2Grasp: Learning Functional Grasps from Web Images of Hand-Object Interactions](https://mlsanity.com/paper/2505.05517/) (2025)
- [Dexterous Manipulation Policies from RGB Human Videos via 3D Hand-Object Trajectory Reconstruction](https://mlsanity.com/paper/2602.09013/) (2026)
- [Get a Grip: Reconstructing Hand-Object Stable Grasps in Egocentric Videos](https://mlsanity.com/paper/2312.15719/) (2023)

## Abstract (arXiv)

We tackle the task of reconstructing hand-object interactions from short video clips. Given an input video, our approach casts 3D inference as a per-video optimization and recovers a neural 3D representation of the object shape, as well as the time-varying motion and hand articulation. While the input video naturally provides some multi-view cues to guide 3D inference, these are insufficient on their own due to occlusions and limited viewpoint variations. To obtain accurate 3D, we augment the multi-view signals with generic data-driven priors to guide reconstruction. Specifically, we learn a diffusion network to model the conditional distribution of (geometric) renderings of objects conditioned on hand configuration and category label, and leverage it as a prior to guide the novel-view renderings of the reconstructed scene. We empirically evaluate our approach on egocentric videos across 6 object categories, and observe significant improvements over prior single-view and multi-view methods. Finally, we demonstrate our system's ability to reconstruct arbitrary clips from YouTube, showing both 1st and 3rd person interactions.