  Recovering dense human poses from images plays a critical role in
establishing an image-to-surface correspondence between RGB images and the 3D
surface of the human body, serving the foundation of rich real-world
applications, such as virtual humans, monocular-to-3d reconstruction. However,
the popular DensePose-COCO dataset relies on a sophisticated manual annotation
system, leading to severe limitations in acquiring the denser and more accurate
annotated pose resources. In this work, we introduce a new 3D human-body model
with a series of decoupled parameters that could freely control the generation
of the body. Furthermore, we build a data generation system based on this
decoupling 3D model, and construct an ultra dense synthetic benchmark
UltraPose, containing around 1.3 billion corresponding points. Compared to the
existing manually annotated DensePose-COCO dataset, the synthetic UltraPose has
ultra dense image-to-surface correspondences without annotation cost and error.
Our proposed UltraPose provides the largest benchmark and data resources for
lifting the model capability in predicting more accurate dense poses. To
promote future researches in this field, we also propose a transformer-based
method to model the dense correspondence between 2D and 3D worlds. The proposed
model trained on synthetic UltraPose can be applied to real-world scenarios,
indicating the effectiveness of our benchmark and model.
