Lately, I’ve been exploring advancements in image-to-image translation, particularly in the context of preserving human pose and body structure during occlusion reconstruction.
A lot of implementations I’ve seen tend to fall short when dealing with:
- Consistent limb positioning
- Realistic lighting continuity
- Texture reconstruction in previously occluded regions
What’s interesting is how some newer platforms have started to combine pose-conditioned GANs with region-aware masking to better maintain structure during generation. One web-based demo I tested — Grey’s Secret Room — appears to implement something along these lines. While the backend is not open, the visual outputs suggest a multi-stage generation process, perhaps integrating attention mechanisms for occluded regions and latent diffusion refinement for skin textures.
It also seems capable of low-res facial reconstructions and multi-sample variation from a single input pose, which hints at some form of identity embedding modulation or class-conditional sampling.
I’m curious if anyone here has worked on — or seen — similar model architectures in the open-source space. Especially interested in pipelines that:
- Accept partial or clothed inputs
- Output realistic textures without distorting proportions
- Can generalize across identity and lighting conditions
Would love to hear thoughts, or any relevant papers / implementations worth reading.