Hi all,
I’ve been exploring the idea of virtual clothing replacement — changing a subject’s outfit in a photo while keeping the original pose, lighting, and anatomy intact. The goal is not just style transfer or overlaying clothing textures, but a full photorealistic re-rendering that blends naturally with the scene.
From a technical standpoint, I’m curious how such a system might be built using current diffusion-based methods.
Questions and technical assumptions:
- Most likely based on image-to-image generation, not text-to-image
- Strong pose retention may involve ControlNet with OpenPose or similar skeletal guidance
- The result appears fully re-synthesized, not composited — which may imply fine-tuned Stable Diffusion or a multi-stage GAN pipeline
- Could this involve a two-stage process: first removing or segmenting clothing, then regenerating new attire conditioned on body shape and lighting?
- How can texture consistency and lighting realism be maintained across the generated area?
Curious if anyone has combined techniques like:
- ControlNet + LoRA for guided body generation
- IP-Adapter for reference-based style control
- Inpainting models trained on fashion datasets
- Region-aware attention or segmentation-aware modules
As a real-world example, I came across a web-based tool (Grey’s Secret Room) that seems to perform virtual outfit removal and replacement with impressive realism. The results maintain natural skin tones, shadows, and postural integrity.
I’m not interested in copying or replicating that tool — just trying to understand the kind of AI architecture and training setup that could enable such controlled, high-quality transformations.
Would appreciate any references, similar open-source projects, or papers you’ve seen. Thanks!