CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects

• URL: http://arxiv.org/abs/2505.21437v1
• Date: Tue, 27 May 2025 17:11:50 GMT
• Title: CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects
• Authors: Huaijin Pi, Zhi Cen, Zhiyang Dou, Taku Komura,
• Abstract summary: Whole-body manipulation of articulated objects is a critical yet challenging task with broad applications in virtual humans and robotics.<n>We propose a novel coordinated diffusion noise optimization framework to achieve realistic whole-body motion.<n>We conduct extensive experiments demonstrating that our method outperforms existing approaches in motion quality and physical plausibility.
• Score: 14.230098033626744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthesizing whole-body manipulation of articulated objects, including body motion, hand motion, and object motion, is a critical yet challenging task with broad applications in virtual humans and robotics. The core challenges are twofold. First, achieving realistic whole-body motion requires tight coordination between the hands and the rest of the body, as their movements are interdependent during manipulation. Second, articulated object manipulation typically involves high degrees of freedom and demands higher precision, often requiring the fingers to be placed at specific regions to actuate movable parts. To address these challenges, we propose a novel coordinated diffusion noise optimization framework. Specifically, we perform noise-space optimization over three specialized diffusion models for the body, left hand, and right hand, each trained on its own motion dataset to improve generalization. Coordination naturally emerges through gradient flow along the human kinematic chain, allowing the global body posture to adapt in response to hand motion objectives with high fidelity. To further enhance precision in hand-object interaction, we adopt a unified representation based on basis point sets (BPS), where end-effector positions are encoded as distances to the same BPS used for object geometry. This unified representation captures fine-grained spatial relationships between the hand and articulated object parts, and the resulting trajectories serve as targets to guide the optimization of diffusion noise, producing highly accurate interaction motion. We conduct extensive experiments demonstrating that our method outperforms existing approaches in motion quality and physical plausibility, and enables various capabilities such as object pose control, simultaneous walking and manipulation, and whole-body generation from hand-only data.

Related papers

• HOSIG: Full-Body Human-Object-Scene Interaction Generation with Hierarchical Scene Perception [57.37135310143126]
  HO SIG is a novel framework for synthesizing full-body interactions through hierarchical scene perception.<n>Our framework supports unlimited motion length through autoregressive generation and requires minimal manual intervention.<n>This work bridges the critical gap between scene-aware navigation and dexterous object manipulation.
  arXiv  Detail & Related papers  (2025-06-02T12:08:08Z)
• Diffgrasp: Whole-Body Grasping Synthesis Guided by Object Motion Using a Diffusion Model [25.00532805042292]
  We propose a simple yet effective framework that jointly models the relationship between the body, hands, and the given object motion sequences.<n>We introduce novel contact-aware losses and incorporate a data-driven, carefully designed guidance.<n> Experimental results demonstrate that our approach outperforms the state-of-the-art method and generates plausible whole-body motion sequences.
  arXiv  Detail & Related papers  (2024-12-30T02:21:43Z)
• ManiDext: Hand-Object Manipulation Synthesis via Continuous Correspondence Embeddings and Residual-Guided Diffusion [36.9457697304841]
  ManiDext is a unified hierarchical diffusion-based framework for generating hand manipulation and grasp poses. Our key insight is that accurately modeling the contact correspondences between objects and hands during interactions is crucial. Our framework first generates contact maps and correspondence embeddings on the object's surface. Based on these fine-grained correspondences, we introduce a novel approach that integrates the iterative refinement process into the diffusion process.
  arXiv  Detail & Related papers  (2024-09-14T04:28:44Z)
• Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation [27.206656215734295]
  We propose a novel Decomposed Vector-Quantized Variational Autoencoder (DVQ-VAE) to generate realistic human grasps. Part-aware decomposed architecture facilitates more precise management of the interaction between each component of hand and object. Our model achieved about 14.1% relative improvement in the quality index compared to the state-of-the-art methods in four widely-adopted benchmarks.
  arXiv  Detail & Related papers  (2024-07-19T06:41:16Z)
• Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method [61.19028558470065]
  We present GazeHOI, the first dataset to capture simultaneous 3D modeling of gaze, hand, and object interactions.<n>To tackle these issues, we propose a stacked gaze-guided hand-object interaction diffusion model, named GHO-Diffusion.<n>We also introduce HOI-Manifold Guidance during the sampling stage of GHO-Diffusion, enabling fine-grained control over generated motions.
  arXiv  Detail & Related papers  (2024-03-24T14:24:13Z)
• InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint [67.6297384588837]
  We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. We demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model.
  arXiv  Detail & Related papers  (2023-11-27T14:32:33Z)
• Object Motion Guided Human Motion Synthesis [22.08240141115053]
  We study the problem of full-body human motion synthesis for the manipulation of large-sized objects. We propose Object MOtion guided human MOtion synthesis (OMOMO), a conditional diffusion framework. We develop a novel system that captures full-body human manipulation motions by simply attaching a smartphone to the object being manipulated.
  arXiv  Detail & Related papers  (2023-09-28T08:22:00Z)
• GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency [57.9920824261925]
  Hands are dexterous and highly versatile manipulators that are central to how humans interact with objects and their environment. modeling realistic hand-object interactions is critical for applications in computer graphics, computer vision, and mixed reality. GRIP is 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.
  arXiv  Detail & Related papers  (2023-08-22T17:59:51Z)
• GraMMaR: Ground-aware Motion Model for 3D Human Motion Reconstruction [61.833152949826946]
  We propose a novel Ground-aware Motion Model for 3D Human Motion Reconstruction, named GraMMaR. GraMMaR learns the distribution of transitions in both pose and interaction between every joint and ground plane at each time step of a motion sequence. It is trained to explicitly promote consistency between the motion and distance change towards the ground.
  arXiv  Detail & Related papers  (2023-06-29T07:22:20Z)
• Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations [61.659439423703155]
  TOHO: Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations. Our method generates continuous motions that are parameterized only by the temporal coordinate. This work takes a step further toward general human-scene interaction simulation.
  arXiv  Detail & Related papers  (2023-03-23T09:31:56Z)
• Task-Generic Hierarchical Human Motion Prior using VAEs [44.356707509079044]
  A deep generative model that describes human motions can benefit a wide range of fundamental computer vision and graphics tasks. We present a method for learning complex human motions independent of specific tasks using a combined global and local latent space. We demonstrate the effectiveness of our hierarchical motion variational autoencoder in a variety of tasks including video-based human pose estimation.
  arXiv  Detail & Related papers  (2021-06-07T23:11:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.