PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human
  Mesh Recovery
Wendi Yang Zihang Jiang Shang Zhao S. Kevin Zhou
With the recent advancements in single-image-based human mesh recovery, there
is a growing interest in enhancing its performance in certain extreme
scenarios, such as occlusion, while maintaining overall model accuracy.
Although obtaining accurately annotated 3D human poses under occlusion is
challenging, there is still a wealth of rich and precise 2D pose annotations
that can be leveraged. However, existing works mostly focus on directly
leveraging 2D pose coordinates to estimate 3D pose and mesh. In this paper, we
present PostoMETRO($\textbf{Pos}$e $\textbf{to}$ken enhanced $\textbf{ME}$sh
$\textbf{TR}$ansf$\textbf{O}$rmer), which integrates occlusion-resilient 2D
pose representation into transformers in a token-wise manner. Utilizing a
specialized pose tokenizer, we efficiently condense 2D pose data to a compact
sequence of pose tokens and feed them to the transformer together with the
image tokens. This process not only ensures a rich depiction of texture from
the image but also fosters a robust integration of pose and image information.
Subsequently, these combined tokens are queried by vertex and joint tokens to
decode 3D coordinates of mesh vertices and human joints. Facilitated by the
robust pose token representation and the effective combination, we are able to
produce more precise 3D coordinates, even under extreme scenarios like
occlusion. Experiments on both standard and occlusion-specific benchmarks
demonstrate the effectiveness of PostoMETRO. Qualitative results further
illustrate the clarity of how 2D pose can help 3D reconstruction. Code will be
made available.