  The 3D Human Pose Estimation (3D HPE) task uses 2D images or videos to
predict human joint coordinates in 3D space. Despite recent advancements in
deep learning-based methods, they mostly ignore the capability of coupling
accessible texts and naturally feasible knowledge of humans, missing out on
valuable implicit supervision to guide the 3D HPE task. Moreover, previous
efforts often study this task from the perspective of the whole human body,
neglecting fine-grained guidance hidden in different body parts. To this end,
we present a new Fine-Grained Prompt-Driven Denoiser based on a diffusion model
for 3D HPE, named \textbf{FinePOSE}. It consists of three core blocks enhancing
the reverse process of the diffusion model: (1) Fine-grained Part-aware Prompt
learning (FPP) block constructs fine-grained part-aware prompts via coupling
accessible texts and naturally feasible knowledge of body parts with learnable
prompts to model implicit guidance. (2) Fine-grained Prompt-pose Communication
(FPC) block establishes fine-grained communications between learned part-aware
prompts and poses to improve the denoising quality. (3) Prompt-driven Timestamp
Stylization (PTS) block integrates learned prompt embedding and temporal
information related to the noise level to enable adaptive adjustment at each
denoising step. Extensive experiments on public single-human pose estimation
datasets show that FinePOSE outperforms state-of-the-art methods. We further
extend FinePOSE to multi-human pose estimation. Achieving 34.3mm average MPJPE
on the EgoHumans dataset demonstrates the potential of FinePOSE to deal with
complex multi-human scenarios. Code is available at
https://github.com/PKU-ICST-MIPL/FinePOSE_CVPR2024.
