Seeking Expertise: AI-Powered Product Photography – Product & Environment Integration

Beginners
1

Hey everyone,

We’re developing an AI-driven product photography solution that goes far beyond simple background replacement. Our goal is to seamlessly integrate real product images into AI-generated environments, ensuring precise lighting, perspective, and reflections that match the scene without breaking realism.

While current tools like Stable Diffusion, ControlNet, and GAN-based solutions provide great generative capabilities, we’re looking for deeper insights into the technical challenges and best approaches for:

  1. Product Integration:
  • How do we ensure the product remains unchanged while blending naturally into AI-generated environments?
  • What are the best ways to preserve surface textures, reflections, and realistic depth when compositing a product into a generated scene?
  • Any thoughts on HDR-aware compositing or multi-view product input for better 3D grounding?
  1. Environmental Enhancement:
  • What methods exist for AI-driven relighting, so the inserted product adopts scene-consistent lighting and shadows?
  • Can we dynamically match materials and reflections so the product interacts with its AI-generated surroundings in a believable way?
  • How would scene-aware depth estimation improve integration?
  1. Bridging Product & Environment:
  • What role can SAM (Segment Anything Model) or NeRF-like techniques play in segmenting and blending elements?
  • How can we use ControlNet or additional conditioning methods to maintain fine-grained control over placement, shadows, and light interaction?
  • Would a hybrid approach (rendering + generative AI) work best, or are there alternatives?

We’re open to discussing architecture, model fine-tuning, or any practical insights that could help push AI-generated product photography closer to real-world studio quality.

Looking forward to hearing your thoughts! :rocket:

P.S. If you have experience working with Stable Diffusion, ComfyUI workflows, or other generative visual AI techniques, we’d love to connect!

1 Like
2

Hey Stormicus, we are few guys from university (undergrads) working on a similar use-case.
Would you like to connect and share knowledge?

Send me an email, matan.orr@mail.huji.ac.il

3

This is a complex process.

I would use a diffusion transformer with multiple layers. You can condition these transformer blocks with their own losses and the diffusion process would be multi step refinement.

block 1. Diffusion of a baseline realism based photo. first auxiliary loss and global task loss.
block 2. Lighting diffusion based on some contextual signal maybe from a prompt.. additional auxliary loss and global loss.
block 3. additional diffusion for realism refinement. - additional auxiliary loss and global loss

The idea would be to condition the network at stages based on the context and the previous layers. The tricky part will be handling the loss signals to ensure proper learning. Alternatively you could use a learned gating mechanism that uses specific experts based on layer or stage in process. But most importantly you would need to target stages with losses and then find the balance in tuning.