CoDA: Coordinated Diffusion Noise Optimization for Whole-Body
  Manipulation of Articulated Objects
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.
683d79b5b84018f0604674a7
Huaijin Pi
claimed_verified
2025-06-02T13:39:28.295Z
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/avatars/ca21e0022e43fae7eddcd4b58aa7406f.svg
Huaijin Pi
user
huaijinpi
683d79b5b84018f0604674a8
Zhi Cen
claimed_verified
2025-06-02T13:39:24.089Z
67e22bdb84513315a91ad112
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Zhi Cen
user
anitacen
683d79b5b84018f0604674a9
Zhiyang Dou
claimed_verified
2025-06-02T13:39:26.083Z
645223fb01d7bd9555ea399a
https://cdn-avatars.huggingface.co/v1/production/uploads/645223fb01d7bd9555ea399a/fVR7XmGg6pMSRKx9sEdvT.png
Zhiyang Dou
user
frankzydou
683d79b5b84018f0604674aa
Taku Komura
CoDA: Coordinated Diffusion Noise Optimization for Whole-Body
  Manipulation of Articulated Objects
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.
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.
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