HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
This work addresses the problem of synthesizing complex human-object interactions for applications in computer graphics and robotics, representing an incremental advance by combining existing diffusion techniques with a structured two-phase approach.
The authors tackled the challenge of generating realistic and plausible human-object interactions (HOI) by introducing HOIDiNi, a text-driven diffusion framework that optimizes in noise space using Diffusion Noise Optimization (DNO), achieving improved contact accuracy, physical validity, and overall quality on the GRAB dataset.
We present HOIDiNi, a text-driven diffusion framework for synthesizing realistic and plausible human-object interaction (HOI). HOI generation is extremely challenging since it induces strict contact accuracies alongside a diverse motion manifold. While current literature trades off between realism and physical correctness, HOIDiNi optimizes directly in the noise space of a pretrained diffusion model using Diffusion Noise Optimization (DNO), achieving both. This is made feasible thanks to our observation that the problem can be separated into two phases: an object-centric phase, primarily making discrete choices of hand-object contact locations, and a human-centric phase that refines the full-body motion to realize this blueprint. This structured approach allows for precise hand-object contact without compromising motion naturalness. Quantitative, qualitative, and subjective evaluations on the GRAB dataset alone clearly indicate HOIDiNi outperforms prior works and baselines in contact accuracy, physical validity, and overall quality. Our results demonstrate the ability to generate complex, controllable interactions, including grasping, placing, and full-body coordination, driven solely by textual prompts. https://hoidini.github.io.