  Novel view synthesis (NVS) of multi-human scenes imposes challenges due to
the complex inter-human occlusions. Layered representations handle the
complexities by dividing the scene into multi-layered radiance fields, however,
they are mainly constrained to per-scene optimization making them inefficient.
Generalizable human view synthesis methods combine the pre-fitted 3D human
meshes with image features to reach generalization, yet they are mainly
designed to operate on single-human scenes. Another drawback is the reliance on
multi-step optimization techniques for parametric pre-fitting of the 3D body
models that suffer from misalignment with the images in sparse view settings
causing hallucinations in synthesized views. In this work, we propose,
GenLayNeRF, a generalizable layered scene representation for free-viewpoint
rendering of multiple human subjects which requires no per-scene optimization
and very sparse views as input. We divide the scene into multi-human layers
anchored by the 3D body meshes. We then ensure pixel-level alignment of the
body models with the input views through a novel end-to-end trainable module
that carries out iterative parametric correction coupled with multi-view
feature fusion to produce aligned 3D models. For NVS, we extract point-wise
image-aligned and human-anchored features which are correlated and fused using
self-attention and cross-attention modules. We augment low-level RGB values
into the features with an attention-based RGB fusion module. To evaluate our
approach, we construct two multi-human view synthesis datasets; DeepMultiSyn
and ZJU-MultiHuman. The results indicate that our proposed approach outperforms
generalizable and non-human per-scene NeRF methods while performing at par with
layered per-scene methods without test time optimization.
