title: CoDA: Coordinated Diffusion Noise Optimization for Whole-Body
    Manipulation of Articulated Objects
summary: 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.
paper_authors._id: 683d79b5b84018f0604674a7
paper_authors.name: Huaijin Pi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T13:39:28.295Z
paper_authors._id: 6836adaf8ec432fdc7e1c550
paper_authors.avatarUrl: /avatars/ca21e0022e43fae7eddcd4b58aa7406f.svg
paper_authors.fullname: Huaijin Pi
paper_authors.type: user
paper_authors.user: huaijinpi
paper_authors._id: 683d79b5b84018f0604674a8
paper_authors.name: Zhi Cen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T13:39:24.089Z
paper_authors._id: 67e22bdb84513315a91ad112
paper_authors.avatarUrl: /avatars/f82ed1b814411f4ac0527cdd2603806e.svg
paper_authors.fullname: Zhi Cen
paper_authors.type: user
paper_authors.user: anitacen
paper_authors._id: 683d79b5b84018f0604674a9
paper_authors.name: Zhiyang Dou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T13:39:26.083Z
paper_authors._id: 645223fb01d7bd9555ea399a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/645223fb01d7bd9555ea399a/fVR7XmGg6pMSRKx9sEdvT.png
paper_authors.fullname: Zhiyang Dou
paper_authors.type: user
paper_authors.user: frankzydou
paper_authors._id: 683d79b5b84018f0604674aa
paper_authors.name: Taku Komura
paper_title: CoDA: Coordinated Diffusion Noise Optimization for Whole-Body
    Manipulation of Articulated Objects
paper_summary: 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.
paper_ai_summary: A coordinated diffusion noise optimization framework improves whole-body manipulation of articulated objects by leveraging specialized diffusion models for body and hand motions and a unified basis point set representation for precise hand-object interaction.
paper_ai_keywords: diffusion noise optimization diffusion models noise-space optimization human kinematic chain basis point sets motion quality physical plausibility object pose control simultaneous walking and manipulation whole-body generation