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

TL;DR

This paper introduces CoDA, a diffusion-based framework for realistic, coordinated whole-body manipulation of articulated objects, improving motion quality and interaction precision through specialized models and unified spatial representations.

Contribution

The paper proposes a novel diffusion noise optimization framework with specialized models and a unified BPS representation for improved coordination and precision in whole-body articulated object manipulation.

Findings

Outperforms existing methods in motion quality and plausibility.

Enables object pose control and simultaneous walking and manipulation.

Achieves high-fidelity, coordinated whole-body motion from hand-only data.

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.