COIN: Control-Inpainting Diffusion Prior for Human and Camera Motion Estimation

TL;DR

COIN introduces a novel control-inpainting diffusion prior and a joint optimization framework to improve global human and camera motion estimation from RGB videos, outperforming existing methods on challenging benchmarks.

Contribution

The paper proposes COIN, a control-inpainting diffusion prior with a new score distillation method and human-scene relation loss for disentangling and accurately estimating motions.

Findings

Outperforms state-of-the-art in global human and camera motion estimation

Achieves 33% improvement in world joint position error on RICH dataset

Demonstrates effectiveness on three challenging benchmarks

Abstract

Estimating global human motion from moving cameras is challenging due to the entanglement of human and camera motions. To mitigate the ambiguity, existing methods leverage learned human motion priors, which however often result in oversmoothed motions with misaligned 2D projections. To tackle this problem, we propose COIN, a control-inpainting motion diffusion prior that enables fine-grained control to disentangle human and camera motions. Although pre-trained motion diffusion models encode rich motion priors, we find it non-trivial to leverage such knowledge to guide global motion estimation from RGB videos. COIN introduces a novel control-inpainting score distillation sampling method to ensure well-aligned, consistent, and high-quality motion from the diffusion prior within a joint optimization framework. Furthermore, we introduce a new human-scene relation loss to alleviate the scale ambiguity by enforcing consistency among the humans, camera, and scene. Experiments on three challenging benchmarks demonstrate the effectiveness of COIN, which outperforms the state-of-the-art methods in terms of global human motion estimation and camera motion estimation. As an illustrative example, COIN outperforms the state-of-the-art method by 33% in world joint position error (W-MPJPE) on the RICH dataset.